Umeå University's logo

umu.sePublications
Change search
Refine search result
12 1 - 50 of 82
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Baranowska-Rataj, Anna
    et al.
    Umeå University, Faculty of Social Sciences, Department of Sociology.
    De Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Ivarsson, Anneli
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Epidemiology and Global Health.
    Does the number of siblings affect health in midlife?: Evidence from the Swedish Prescribed Drug Register2016In: Demographic Research, ISSN 1435-9871, Vol. 35, p. 1259-1302, article id 43Article in journal (Refereed)
    Abstract [en]

    Background: In many societies, growing up in a large family is associated with receiving less parental time, attention, and financial support. As a result, children with a large number of siblings may have worse physical and mental health outcomes than children with fewer siblings.

    Objective: Our objective is to examine the long-term causal effects of sibship size on physical and mental health in modern Sweden.

    Methods: We employ longitudinal data covering the entire Swedish population from the Multigenerational Register and the Medical Birth Register. This data includes information on family size and on potential confounders such as parental background. We use the Prescribed Drug Register to identify the medicines that have been prescribed and dispensed. We use instrumental variable models with multiple births as instruments to examine the causal effects of family size on the health outcomes of children, as measured by receiving medicines at age 45.

    Results: Our results indicate that in Sweden, growing up in a large family does not have a detrimental effect on physical and mental health in midlife.

    Contribution: We provide a systematic overview of the health-related implications of growing up in a large family. We adopt a research design that gives us the opportunity to make causal inferences about the long-term effects of family size. Moreover, our paper provides evidence on the links between family size and health outcomes in the context of a developed country that implements policies oriented towards reducing social inequalities in health and other living conditions.

    Download full text (pdf)
    fulltext
  • 2. Barban, Nicola
    et al.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Lundholm, Emma
    Umeå University, Faculty of Social Sciences, Department of Geography and Economic History. Umeå University, Faculty of Social Sciences, Centre for Demographic and Ageing Research (CEDAR).
    Svensson, Ingrid
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Billari, F. C.
    Causal Effects of the Timing of Life-course Events: Age at Retirement and Subsequent Health2020In: Sociological Methods & Research, ISSN 0049-1241, E-ISSN 1552-8294, Vol. 49, no 1, p. 216-249Article in journal (Refereed)
    Abstract [en]

    In this article, we combine the extensive literature on the analysis of life-course trajectories as sequences with the literature on causal inference and propose a new matching approach to investigate the causal effect of the timing of life-course events on subsequent outcomes. Our matching approach takes into account pre-event confounders that are both time-independent and time-dependent as well as life-course trajectories. After matching, treated and control individuals can be compared using standard statistical tests or regression models. We apply our approach to the study of the consequences of the age at retirement on subsequent health outcomes, using a unique data set from Swedish administrative registers. Once selectivity in the timing of retirement is taken into account, effects on hospitalization are small, while early retirement has negative effects on survival. Our approach also allows for heterogeneous treatment effects. We show that the effects of early retirement differ according to preretirement income, with higher income individuals tending to benefit from early retirement, while the opposite is true for individuals with lower income.

    Download full text (pdf)
    fulltext
  • 3. Bask, Mikael
    et al.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Statistics.
    Characterizing the degree of stability of non-linear dynamic models2002In: Studies in Nonlinear Dynamics and Econometrics, Vol. 6, no 1Article in journal (Refereed)
  • 4. Bask, Mikael
    et al.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Statistics.
    EMU and the Stability and Volatility of Foreign Exchange: Some Empirical Evidence2005In: Chaos, Solitons & Fractals, Vol. 25, p. 737-750Article in journal (Refereed)
  • 5.
    Broström, Göran
    et al.
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Lundin, Mathias
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Att läsa i Umeå är ett bra val2005In: Västerbottens-Kuriren, no 9 december, p. 4-Article in journal (Other (popular science, discussion, etc.))
  • 6.
    Brännäs, Kurt
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Economics.
    De Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Economics.
    Generalized method of moment and indirect estimation of the ARasMA model1998In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 13, no 4, p. 485-494Article in journal (Refereed)
    Abstract [en]

    Estimation in nonlinear time series models has mainly been performed by least squares or maximum likelihood (ML) methods. The paper suggests and studies the performance of generalized method of moments (GMM) and indirect estimators for the autoregressive asymmetric moving average model. Both approaches are easy to implement and perform well numerically. In a Monte Carlo study it is found that the MSE properties of GMM are close to those of ML. The indirect estimator performs poorly in this respect. On the other hand, the three estimation techniques lead to fairly similar power functions for a linearity test.

  • 7. Cantoni, Eva
    et al.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Statistics.
    Non-parametric adjustment for covariates when estimating a treatment effect2004Report (Other academic)
  • 8. Cantoni, Eva
    et al.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Statistics.
    Non-parametric adjustment for covariates when estimating a treatment effect2006In: Journal of Nonparametric Statistics, Vol. 18, p. 227-244Article in journal (Refereed)
  • 9.
    Cantoni, Eva
    et al.
    Research Center for Statistics and Geneva School of Economics and Management, University of Geneva, Geneva, Switzerland.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Semiparametric inference with missing data: robustness to outliers and model misspecification2020In: Econometrics and Statistics, ISSN 2452-3062, Vol. 16, p. 108-120Article in journal (Refereed)
    Abstract [en]

    Classical semiparametric inference with missing outcome data is not robust to contamination of the observed data and a single observation can have arbitrarily large influence on estimation of a parameter of interest. This sensitivity is exacerbated when inverse probability weighting methods are used, which may overweight contaminated observations. Inverse probability weighted, double robust and outcome regression estimators of location and scale parameters are introduced, which are robust to contamination in the sense that their influence function is bounded. Asymptotic properties are deduced and finite sample behaviour studied. Simulated experiments show that contamination can be more serious a threat to the quality of inference than model misspecification. An interesting aspect of the results is that the auxiliary outcome model used to adjust for ignorable missingness by some of the estimators, is also useful to protect against contamination. Both adjustment to ignorable missingness and protection against contamination are achieved through weighting schemes. A case study illustrates how the resulting weights can be studied to gain insights on how the two different weighting schemes interact.

    Download full text (pdf)
    arXivVersion
  • 10. Chaparro, M Pia
    et al.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Häggström, Jenny
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Ivarsson, Anneli
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Epidemiology and Global Health.
    Lindgren, Urban
    Umeå University, Faculty of Social Sciences, Department of Geography and Economic History, Economic and social geography.
    Nilsson, Karina
    Umeå University, Faculty of Social Sciences, Department of Sociology.
    Koupil, Ilona
    Childhood family structure and women's adult overweight risk: A longitudinal study2017In: Scandinavian Journal of Public Health, ISSN 1403-4948, E-ISSN 1651-1905, Vol. 45, no 5, p. 511-519Article in journal (Refereed)
    Abstract [en]

    AIM: The aim of this study was to investigate whether women's adult overweight and obesity risk was associated with their childhood family structure, measured as their mothers' marital status history, during the women's first 18 years of life.

    METHODS: Using linked register data, we analyzed 30,584 primiparous women born in Sweden in 1975 who were between 19-35 years of age when their height and pre-pregnancy weight was recorded. The outcomes were women's overweight/obesity (body mass index (BMI) ≥ 25 kg/m(2)) and obesity (BMI ≥ 30 kg/m(2)) and the predictor was mothers' marital status history, which was summarized using sequence analysis. We carried out nested logistic regression models adjusting for women's age and maternal sociodemographic characteristics.

    RESULTS: Mothers' marital status history was summarized into six clusters: stable marriage, stable cohabitation, married then divorcing, cohabiting then separating, varied transitions, and not with father. In fully adjusted models and compared with women whose mothers belonged to the stable marriage cluster: (1) women whose mothers belonged to the other marital status clusters had higher odds of overweight/obesity (odds ratio (OR) ranging 1.15-1.19; p < 0.05); and (2) women whose mothers belonged to the stable cohabitation (OR = 1.31; 95% confidence interval (CI) = 1.14-1.52), cohabiting then separating (OR = 1.23; 95% CI = 1.01-1.49), varied transitions (OR = 1.24; 95% CI = 1.11-1.39), and not with father (OR = 1.24; 95% CI = 1.00-1.54) clusters had higher odds of obesity.

    CONCLUSIONS: Women whose mothers were not in stable marriage relationships had higher odds of being overweight or obese in adulthood. The finding that even women raised in the context of stable cohabitation had higher odds of being overweight or obese is intriguing as these relationships are socially accepted in Sweden.

  • 11.
    Chaparro, M Pia
    et al.
    Centre for Health Equity Studies (CHESS).
    Ivarsson, Anneli
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Epidemiology and Global Health.
    Koupil, Ilona
    Centre for Health Equity Studies (CHESS).
    Nilsson, Karina
    Umeå University, Faculty of Social Sciences, Department of Sociology.
    Häggström, Jenny
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Lindgren, Urban
    Umeå University, Faculty of Social Sciences, Department of Geography and Economic History.
    Regional inequalities in overweight and obesity among first-time pregnant women in Sweden, 1992–20102015In: 22nd European Congress on Obesity (ECO2015), Prague, Czech Republic, May 6-9, 2015: abstracts, S. Karger, 2015, Vol. 8: suppl 1, p. 119-119Conference paper (Refereed)
  • 12.
    Chaparro, M. Pia
    et al.
    Centre for Health Equity Studies (CHESS).
    Ivarsson, Anneli
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Epidemiology and Global Health.
    Koupil, Ilona
    Centre for Health Equity Studies (CHESS).
    Nilsson, Karina
    Umeå University, Faculty of Social Sciences, Department of Sociology.
    Häggström, Jenny
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Lindgren, Urban
    Umeå University, Faculty of Social Sciences, Department of Geography and Economic History.
    Regional inequalities in pre-pregnancy overweight and obesity in Sweden, 1992, 2000, and 20102015In: Scandinavian Journal of Public Health, ISSN 1403-4948, E-ISSN 1651-1905, Vol. 43, no 5, p. 534-539Article in journal (Refereed)
    Abstract [en]

    Aims: To investigate regional differences and time trends in women’s overweight and obesity in Sweden. Methods: Using datafrom the Swedish Medical Birth Register (women aged ⩾18 years, first pregnancy only) and the Total Population Registeraccessed through the Umeå SIMSAM Lab, age-standardized prevalence of pre-pregnancy overweight/obesity (BMI ⩾ 25 kg/m2) and obesity (BMI ⩾ 30 kg/m2) were estimated by county for the years 1992, 2000, and 2010. Maps were created usingArcMap v10.2.2 to display regional variations over time and logistic regression analyses were used to assess if the observedtrends were significant. Results: The prevalence of pre-pregnancy overweight/obesity and obesity increased significantly inall Swedish counties between 1992, and 2010. In 2010, Södermanland and Gotland exhibited the highest age-standardizedoverweight/obesity (39.7%) and obesity (15.1%) prevalence, respectively. The sharpest increases between 1992 and 2010were observed in Västerbotten for overweight/obesity (75% increase) and in Gotland for obesity (233% increase). Across theyears, Stockholm had the lowest prevalence of overweight/obesity (26.3% in 2010) and obesity (7.3% in 2010) and one ofthe least steep increases in prevalence of both between 1992 and 2010. Conclusions: Substantial regional differencesin pre-pregnancy overweight and obesity prevalence are apparent in Sweden. Further research should elucidatethe mechanisms causing these differences.

  • 13.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Statistics.
    Guaranteed-content prediction intervals for non-linear autoregressions2001In: Journal of Forecasting, Vol. 20, no 4, p. 265-272Article in journal (Refereed)
  • 14.
    de Luna, Xavier
    et al.
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Daunfeldt, Sven-Olov
    Central bank independence and price stability: evidence from OECD-countries2008In: Oxford Economic Papers, ISSN 0030-7653, E-ISSN 1464-3812, Vol. 60, no 3, p. 410-422Article in journal (Refereed)
  • 15.
    de Luna, Xavier
    et al.
    Umeå University, Faculty of Social Sciences, Statistics.
    Daunfeldt, Sven-Olov
    The Efficacy and Cost of Regime Shifts in Inflation Policies: Evidence from New Zealand and Sweden2001In: Applied Economics, Vol. 33, p. 217-224Article in journal (Refereed)
  • 16.
    de Luna, Xavier
    et al.
    Umeå University, Faculty of Social Sciences, Statistics.
    Forslund, Anders
    Liljeberg, Linus
    Effekter av yrkesinriktad arbetsmarknadsutbildning för deltagare under perioden 2002-042008Report (Other academic)
  • 17.
    de Luna, Xavier
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Fowler, Philip
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Proxy variables and nonparametric identification of causal effectsManuscript (preprint) (Other academic)
    Abstract [en]

    Proxy variables are often used in linear regression models with the aim of removing potential confounding bias. In this paper we formalise proxy variables within the potential outcome framework, giving conditions under which it can be shown that causal effects are nonparametrically identified. We characterise two types of proxy variables and give concrete examples where the proxy conditions introduced may hold by design.

  • 18.
    de Luna, Xavier
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Fowler, Philip
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Johansson, Per
    Proxy variables and nonparametric identification of causal effects2017In: Economics Letters, ISSN 0165-1765, E-ISSN 1873-7374, Vol. 150, p. 152-154Article in journal (Refereed)
    Abstract [en]

    Proxy variables are often used in linear regression models with the aim of removing potential confounding bias. In this paper we formalise proxy variables within the potential outcomes framework, giving conditions under which it can be shown that causal effects are nonparametrically identified. We characterise two types of proxy variables and give concrete examples where the proxy conditions introduced may hold by design.

  • 19.
    de Luna, Xavier
    et al.
    Umeå University, Faculty of Social Sciences, Statistics.
    Genton, Marc G
    Predictive spatio-temporal models for spatially sparse environmental data2005In: Statistica Sinica, Vol. 15, p. 547-568Article in journal (Refereed)
  • 20.
    de Luna, Xavier
    et al.
    Umeå University, Faculty of Social Sciences, Statistics.
    Genton, Marc G.
    Robust Simulation-Based Estimation of ARMA Models2001In: Journal of Computational and Graphical Statistics, Vol. 10, p. 370-387Article in journal (Refereed)
    Abstract [en]

    This article proposes a new approach to the robust estimation of a mixed autoregressive and moving average (ARMA) model. It is based on the indirect inference method that originally was proposed for models with an intractable likelihood function. The estimation algorithm proposed is based on an auxiliary autoregressive representation whose parameters are first estimated on the observed time series and then on data simulated from the ARMA model. To simulate data the parameters of the ARMA model have to be set. By varying these we can minimize a distance between the simulation-based and the observation-based auxiliary estimate. The argument of the minimum yields then an estimator for the parameterization of the ARMA model. This simulation-based estimation procedure inherits the properties of the auxiliary model estimator. For instance, robustness is achieved with GM estimators. An essential feature of the introduced estimator, compared to existing robust estimators for ARMA models, is its theoretical tractability that allows us to show consistency and asymptotic normality. Moreover, it is possible to characterize the influence function and the breakdown point of the estimator. In a small sample Monte Carlo study it is found that the new estimator performs fairly well when compared with existing procedures. Furthermore, with two real examples, we also compare the proposed inferential method with two different approaches based on outliers detection.

  • 21.
    de Luna, Xavier
    et al.
    Umeå University, Faculty of Social Sciences, Statistics.
    Genton, Marc G.
    Simulation-based Inference for Simultaneous Processes on Regular Lattices2002In: Statistics and Computing, Vol. 12, p. 125-134Article in journal (Refereed)
  • 22.
    de Luna, Xavier
    et al.
    Umeå University, Faculty of Social Sciences, Statistics.
    Genton, Marc G
    Spatio-temporal autoregressive models for US unemployment rate2004In: Spatial and Spatiotemporal Econometrics, Elsevier, Amsterdam , 2004, p. 279-294Chapter in book (Refereed)
  • 23.
    de Luna, Xavier
    et al.
    Umeå University, Faculty of Social Sciences, Statistics.
    Johansson, Per
    Comment on "Maximum likelihood estimation in semiparametric regression models with censored data" by D. Zeng and D.Y. Lin2007In: Journal of the Royal Statistical Society, Series B, Vol. 69, no 4, p. 554-555Article in journal (Other academic)
  • 24.
    de Luna, Xavier
    et al.
    Umeå University, Faculty of Social Sciences, Statistics.
    Johansson, Per
    Exogeneity in structural equation models2006In: Journal of Econometrics, Vol. 132, p. 527-543Article in journal (Refereed)
    Abstract [en]

    The practical relevance of several concepts of exogeneity of treatments

    for the estimation of causal parameters based on observational data are

    discussed. We show that the traditional concepts, such as strong ignorability

    and weak and super-exogeneity, are too restrictive if interest lies

    in average effects (i.e. not on distributional effects of the treatment). We

    suggest a new definition of exogeneity, KL−exogeneity. It does not rely

    on distributional assumptions and is not based on counterfactual random

    variables. As a consequence it can be empirically tested using a proposed

    test that is simple to implement and is distribution-free.

  • 25.
    de Luna, Xavier
    et al.
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Johansson, Per
    Uppsala University.
    Graphical diagnostics of endogeneity2008In: Modelling and Evaluating Treatment Effects in Econometrics / [ed] Tom Fomby, R. Carter Hill, Daniel L. Millimet, Jeffrey A. Smith, Edward J. Vytlacil, Emerald Group Publishing Limited, 2008, p. 147-166Chapter in book (Other academic)
    Abstract [en]

    We show that in sorting cross-sectional data, the endogeneity of a variable may be successfully detected by graphically examining the cumulative sum of the recursive residuals. Moreover, the sign of the bias implied by the endogeneity may be deducible through such graphs. In general, instrumental variables are needed to implement the graphical test. However, when a continuous or ordered (e.g. years of schooling) variable is suspected to be endogenous, a graphical test for misspecification due to endogeneity (e.g. self-selection) can be obtained without instrumental variables.

  • 26.
    de Luna, Xavier
    et al.
    Umeå University, Faculty of Social Sciences, Statistics.
    Johansson, Per
    Matching estimators for the effect of a treatment on survival times2007Report (Other academic)
  • 27.
    de Luna, Xavier
    et al.
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Johansson, Per
    Department of Economics, Uppsala University, Uppsala, Sweden; Institute of Labour Market Policy Evaluation, Uppsala, Sweden.
    Non-parametric inference for the effect of a treatment on survival times with application in the health and social sciences2010In: Journal of Statistical Planning and Inference, ISSN 0378-3758, E-ISSN 1873-1171, Vol. 140, no 7, p. 2122-2137Article in journal (Refereed)
    Abstract [en]

    In this paper we perform inference on the effect of a treatment on survival times in studies where the treatment assignment is not randomized and the assignment time is not known in advance. Two such studies are discussed: a heart transplant program and a study of Swedish unemployed eligible for employment subsidy. We estimate survival functions on a treated and a control group which are made comparable through matching on observed covariates. The inference is performed by conditioning on waiting time to treatment, that is, time between the entrance in the study and treatment. This can be done only when sufficient data are available. In other cases, averaging over waiting times is a possibility, although the classical interpretation of the estimated survival functions is lost unless hazards are not functions of waiting time. To show unbiasedness and to obtain an estimator of the variance, we build on the potential outcome framework, which was introduced by J. Neyman in the context of randomized experiments, and adapted to observational studies by D.B. Rubin. Our approach does not make parametric or distributional assumptions. In particular, we do not assume proportionality of the hazards compared. Small sample performance of the estimator and a derived test of no treatment effect are studied in a Monte Carlo study.

  • 28.
    de Luna, Xavier
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Johansson, Per
    Department of Economics, Uppsala University, Uppsala, Sweden; Institute for Evaluation of Labour Market and Education Policy, Uppsala, Sweden.
    Testing for the Unconfoundedness Assumption Using an Instrumental Assumption2014In: Journal of Causal Inference, ISSN 2193-3677, E-ISSN 2193-3685, Vol. 2, no 2, p. 187-199Article in journal (Refereed)
    Abstract [en]

    The identification of average causal effects of a treatment in observational studies is typically based either on the unconfoundedness assumption (exogeneity of the treatment) or on the availability of an instrument. When available, instruments may also be used to test for the unconfoundedness assumption. In this paper, we present a set of assumptions on an instrumental variable which allows us to test for the unconfoundedness assumption, although they do not necessarily yield nonparametric identification of an average causal effect. We propose a test for the unconfoundedness assumption based on the instrumental assumptions introduced and give conditions under which the test has power. We perform a simulation study and apply the results to a case study where the interest lies in evaluating the effect of job practice on employment.

    Download full text (pdf)
    fulltext
  • 29.
    de Luna, Xavier
    et al.
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Lundin, Mathias
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Sensitivity analysis of the unconfoundedess assumption in observational studies2009Report (Other academic)
  • 30.
    de Luna, Xavier
    et al.
    Umeå University, Faculty of Social Sciences, Statistics.
    Skouras, Kostas
    Choosing a Model Selection Strategy2003In: Scandinavian Journal of Statistics, Vol. 30, p. 113-128Article in journal (Refereed)
  • 31.
    de Luna, Xavier
    et al.
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Stenberg, Anders
    Institutet för Social forskning, Stockholms universitet.
    Westerlund, Olle
    Umeå University, Faculty of Social Sciences, Department of Economics.
    Can adult education delay retirement from the labour market?2010Report (Other academic)
  • 32.
    de Luna, Xavier
    et al.
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Stenberg, Anders
    SOFI, Stockholm University.
    Westerlund, Olle
    Umeå University, Faculty of Social Sciences, Department of Economics.
    Can adult education delay retirement from the Labour Market?2008Report (Other academic)
    Abstract [en]

    Several studies have suggested that education is associated with later retirement from the labour market. In this paper, we examine whether adult education, involving enrolees aged 42 or above, delays retirement to potentially increase labour force participation among the elderly. With Swedish register data of transcripts from adult education and an-nual earnings, which encompasses 1979-2004 and 1982-2004 respectively, we exploit the fact that adult education is a large-scale phenomenon in Sweden and construct a measure of the timing of the transition from being self-supported by productive work to being supported by pension transfers. We match samples of treated and controls on the propen-sity score and use non-parametric estimation of survival rates. The results indicate that adult education has no effect on the timing of the retirement from the labour force. This can be contrasted with the fact that adult education is one of the cornerstones of the OECD strategy for “active ageing” and the European Union’s “Lisbon strategy” for growth and jobs.

  • 33.
    de Luna, Xavier
    et al.
    Umeå University, Faculty of Social Sciences, Statistics.
    Waernbaum, Ingeborg
    Umeå University, Faculty of Social Sciences, Statistics.
    Covariate selection for non-parametric estimation of treatment effects2005Report (Other academic)
  • 34.
    de Luna, Xavier
    et al.
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Waernbaum, Ingeborg
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Richardson, Thomas
    Department of Statistics, University of Washington, Box 354322, Washington 98195-4322 Seattle, U.S.A..
    Covariate selection for the non-parametric estimation of an average treatment effect2011In: Biometrika, ISSN 0006-3444, E-ISSN 1464-3510, Vol. 98, no 4, p. 861-875Article in journal (Refereed)
    Abstract [en]

    Observational studies in which the effect of a nonrandomized treatment on an outcome of interest is estimated are common in domains such as labour economics and epidemiology. Such studies often rely on an assumption of unconfounded treatment when controlling for a given set of observed pre-treatment covariates. The choice of covariates to control in order to guarantee unconfoundedness should primarily be based on subject matter theories, although the latter typically give only partial guidance. It is tempting to include many covariates in the controlling set to try to make the assumption of an unconfounded treatment realistic. Including unnecessary covariates is suboptimal when the effect of a binary treatment is estimated nonparametrically. For instance, when using a n1/2-consistent estimator, a loss of efficiency may result from using covariates that are irrelevant for the unconfoundedness assumption. Moreover, bias may dominate the variance when many covariates are used. Embracing the Neyman–Rubin model typically used in conjunction with nonparametric estimators of treatment effects, we characterize subsets from the original reservoir of covariates that are minimal in the sense that the treatment ceases to be unconfounded given any proper subset of these minimal sets. These subsets of covariates are shown to be identified under mild assumptions. These results lead us to propose data-driven algorithms for the selection of minimal sets of covariates.

  • 35.
    Ecker, Kreske
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Baranowska-Rataj, Anna
    Umeå University, Faculty of Social Sciences, Department of Sociology. Umeå University, Faculty of Social Sciences, Centre for Demographic and Ageing Research (CEDAR).
    Brydsten, Anna
    Umeå University, Faculty of Social Sciences, Department of Sociology.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    The effects of initial firm age on earnings trajectoriesManuscript (preprint) (Other academic)
  • 36.
    Ecker, Kreske
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Schelin, Lina
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Causal inference with a functional outcome2024In: The Journal of the Royal Statistical Society, Series C: Applied Statistics, ISSN 0035-9254, E-ISSN 1467-9876, Vol. 73, no 1, p. 221-240Article in journal (Refereed)
    Abstract [en]

    This article presents methods to study the causal effect of a binary treatment on a functional outcome with observational data. We define a Functional Average Treatment Effect (FATE) and develop an outcome regression estimator. We show how to obtain valid inference on the FATE using simultaneous confidence bands, which cover the FATE with a given probability over the entire domain. Simulation experiments illustrate how the simultaneous confidence bands take the multiple comparison problem into account. Finally, we use the methods to infer the effect of early adult location on subsequent income development for one Swedish birth cohort.

    Download full text (pdf)
    fulltext
  • 37.
    Ecker, Kreske
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Westerlund, Olle
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE).
    Regional differences in initial labour market conditions and dynamics in lifetime income trajectories2022In: Longitudinal and Life Course Studies, E-ISSN 1757-9597, Vol. 13, no 3, p. 352-379Article in journal (Refereed)
    Abstract [en]

    We use longitudinal register data from Sweden to study patterns and dynamics in lifetime income trajectories. We examine divergences in these income trajectories by local economic conditions at labour market entry, in combination with other factors such as gender, education level and socio-economic background. We cannot assume that these relationships are constant over the course of individuals’ working lives. Therefore, we use methods from functional data analysis, allowing for a time-varying relationship between income and the explanatory variables. Our results show a large degree of heterogeneity in how lifetime income trajectories develop for different subgroups. We find that, for men, entering the labour market in an urban area is associated with higher cumulative lifetime income, especially later in life. The exception is men with only primary education, for whom those starting their working lives in a large city have lower incomes on average. This divergence increases in size over time. Women who enter into a large urban labour market receive higher lifetime income at all education levels. This relationship is strongest for women with primary education but decreases in strength over time for these women.

    Download full text (pdf)
    fulltext
  • 38.
    Ecker, Kreske
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Liu, Xijia
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Schelin, Lina
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Double robust estimation of functional outcomes with data missing at randomManuscript (preprint) (Other academic)
  • 39.
    Edström, Filip
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Hellström, Thomas
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Robot causal discovery aided by human interaction2023In: 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), IEEE, 2023, p. 1731-1736Conference paper (Refereed)
    Abstract [en]

    Causality is relatively unexplored in robotics even if it is highly relevant, in several respects. In this paper, we study how a robot’s causal understanding can be improved by allowing the robot to ask humans causal questions. We propose a general algorithm for selecting direct causal effects to ask about, given a partial causal representation (using partially directed acyclic graphs, PDAGs) obtained from observational data. We propose three versions of the algorithm inspired by different causal discovery techniques, such as constraint-based, score-based, and interventions. We evaluate the versions in a simulation study and our results show that asking causal questions improves the causal representation over all simulated scenarios. Further, the results show that asking causal questions based on PDAGs discovered from data provides a significant improvement compared to asking questions at random, and the version inspired by score-based techniques performs particularly well over all simulated experiments.

  • 40.
    Fowler, Philip
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Johansson, Per
    Department of Statistics, Uppsala University.
    Ornstein, Petra
    The Public Employment Service, Stockholm.
    Bill, Sofia
    The Swedish Social Insurance Agency, Stockholm.
    Bengtsson, Peje
    The Swedish Social Insurance Agency, Stockholm.
    Evaluation of a Vocational Rehabilitation: Using a Proxy for Unobserved ConfoundersManuscript (preprint) (Other academic)
  • 41.
    Fowler, Philip
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Johansson, Per
    Ornstein, Petra
    Bill, Sofia
    Bengtsson, Peje
    Study protocol for the evaluation of a vocational rehabilitation2017In: Observational Studies, Vol. 3, p. 1-27Article in journal (Refereed)
    Abstract [en]

    This paper presents a study protocol for the evaluation of a vocational rehabilitation, namely a collaboration between the Swedish Social Insurance Agency and the Public Employment Service, where individuals needing support to regain work ability were called to a joint assessment meeting. This protocol describes a matching study design using a lasso algorithm, where we do not have access to outcome data on work ability for the treated. The matching design is based on a collection of health and socio-economic covariates measured at baseline. We also have access to a prognosis made by caseworkers on the expected length of the individual sick leave. This prognosis variable is, we argue, a proxy variable for potential unmeasured confounders. We present results showing balance achieved on observed covariates.

    Download full text (pdf)
    fulltext
  • 42.
    Fowler, Philip
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Waernbaum, Ingeborg
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Data-driven Coarsening of Covariates for Causal InferenceManuscript (preprint) (Other academic)
  • 43.
    Genbäck, Minna
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Bounds and sensitivity analysis when estimating average treatment effects with imputation and double robust estimatorsManuscript (preprint) (Other academic)
    Abstract [en]

    When estimating average causal effects of treatments with observational data, scientists often rely on the assumption of unconfoundedness. We propose a sensitivity analysis for imputation estimators and doubly robust estimators, based on bounds (defining an identification interval) for the causal effect of interest, which allow for unobserved confounders. The bounds are derived from the bias of the estimators, expressed as a function of a sensitivity parameter. We describe how such bounds can take into account sampling variation, thereby yielding an uncertainty interval. We are also able to contrast the size of potential bias due to violation of the unconfoundedness assumption, to the misspecification of the models used to explain outcome with the observed covariates. While the latter bias can in principle be made arbitrarily small with increasing sample size (by increasing the flexibility of the models used), the bias due to unobserved confounding does not disappear with increasing sample size. Through numerical experiments we illustrate the relative size of the biases due to unobserved confounders and model misspecification, as well as the empirical coverage of the uncertainty interval on which the proposed sensitivity analysis is based.

  • 44.
    Genbäck, Minna
    et al.
    Umeå University, Faculty of Social Sciences, Centre for Demographic and Ageing Research (CEDAR).
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Causal inference accounting for unobserved confounding after outcome regression and doubly robust estimation2019In: Biometrics, ISSN 0006-341X, E-ISSN 1541-0420, Vol. 75, no 2, p. 506-515Article in journal (Refereed)
    Abstract [en]

    Causal inference with observational data can be performed under an assumption of no unobserved confounders (unconfoundedness assumption). There is, however, seldom clear subject-matter or empirical evidence for such an assumption. We therefore develop uncertainty intervals for average causal effects based on outcome regression estimators and doubly robust estimators, which provide inference taking into account both sampling variability and uncertainty due to unobserved confounders. In contrast with sampling variation, uncertainty due to unobserved confounding does not decrease with increasing sample size. The intervals introduced are obtained by modeling the treatment assignment mechanism and its correlation with the outcome given the observed confounders, allowing us to derive the bias of the estimators due to unobserved confounders. We are thus also able to contrast the size of the bias due to violation of the unconfoundedness assumption, with bias due to misspecification of the models used to explain potential outcomes. This is illustrated through numerical experiments where bias due to moderate unobserved confounding dominates misspecification bias for typical situations in terms of sample size and modeling assumptions. We also study the empirical coverage of the uncertainty intervals introduced and apply the results to a study of the effect of regular food intake on health. An R-package implementing the inference proposed is available.

  • 45.
    Genbäck, Minna
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Ng, Nawi
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Epidemiology and Global Health.
    Stanghellini, Elena
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Predictors of decline in self-reported health: addressing non-ignorable dropout in longitudinal studies of ageing2018In: European Journal of Ageing, ISSN 1613-9372, E-ISSN 1613-9380, Vol. 15, no 2, p. 211-220Article in journal (Refereed)
    Abstract [en]

    Predictors of decline in health in older populations have been investigated in multiple studies before. Most longitudinal studies of aging, however, assume that dropout at follow-up is ignorable (missing at random) given a set of observed characteristics at baseline. The objective of this study was to address non-ignorable dropout in investigating predictors of declining self-reported health (SRH) in older populations (50 years or older) in Sweden, the Netherlands, and Italy. We used the SHARE panel survey, and since only 2895 out of the original 5657 participants in the survey 2004 were followed up in 2013, we studied whether the results were sensitive to the expectation that those dropping out have a higher proportion of decliners in SRH. We found that older age and a greater number of chronic diseases were positively associated with a decline in self-reported health in the three countries studies here. Maximum grip strength was associated with decline in self-reported health in Sweden and Italy, and self-reported limitations in normal activities due to health problems were associated with decline in self-reported health in Sweden. These results were not sensitive to non-ignorable dropout. On the other hand, although obesity was associated with decline in a complete case analysis, this result was not confirmed when performing a sensitivity analysis to non-ignorable dropout. The findings, thereby, contribute to the literature in understanding the robustness of longitudinal study results to non-ignorable dropout while considering three different population samples in Europe.

    Download full text (pdf)
    fulltext
  • 46.
    Genbäck, Minna
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Stanghellini, Elena
    University of Perugia, Perugia, Italy.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Uncertainty intervals for regression parameters with non-ignorable missingness in the outcome2015In: Statistical papers, ISSN 0932-5026, E-ISSN 1613-9798, Vol. 56, no 3, p. 829-847Article in journal (Refereed)
    Abstract [en]

    When estimating regression models with missing outcomes, scientists usually have to rely either on a missing at random assumption (missing mechanism is independent from the outcome given the observed variables) or on exclusion restrictions (some of the covariates affecting the missingness mechanism do not affect the outcome). Both these hypotheses are controversial in applications since they are typically not testable from the data. The alternative, which we pursue here, is to derive identification sets (instead of point identification) for the parameters of interest when allowing for a missing not at random mechanism. The non-ignorability of this mechanism is quantified with a parameter. When the latter can be bounded with a priori information, a bounded identification set follows. Our approach allows the outcome to be continuous and unbounded and relax distributional assumptions. Estimation of the identification sets can be performed via ordinary least squares and sampling variability can be incorporated yielding uncertainty intervals achieving a coverage of at least (1-α) probability. Our work is motivated by a study on predictors of body mass index (BMI) change in middle age men allowing us to identify possible predictors of BMI change even when assuming little on the missing mechanism.

    Download full text (pdf)
    fulltext
  • 47.
    Ghosh, Trinetri
    et al.
    Pennsylvania State University.
    Ma, Yanyuan
    Pennsylvania State University.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Sufficient Dimension Reduction for Feasible and Robust Estimation of Average Causal Effect2021In: Statistica sinica, ISSN 1017-0405, E-ISSN 1996-8507, Vol. 31, no 2, p. 821-842Article in journal (Refereed)
    Abstract [en]

    To estimate the treatment effect in an observational study, we use a semiparametric locally efficient dimension-reduction approach to assess the treatment assignment mechanisms and average responses in both the treated and the non-treated groups. We then integrate our results using imputation, inverse probability weighting, and doubly robust augmentation estimators. Doubly robust estimators are locally efficient, and imputation estimators are super-efficient when the response models are correct. To take advantage of both procedures, we introduce a shrinkage estimator that combines the two. The proposed estimators retains the double robustness property, while improving on the variance when the response model is correct. We demonstrate the performance of these estimators using simulated experiments and a real data set on the effect of maternal smoking on baby birth weight.

    Download full text (pdf)
    fulltext
  • 48.
    Gorbach, Tetiana
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Inference for partial correlation when data are missing not at random2018In: Statistics and Probability Letters, ISSN 0167-7152, E-ISSN 1879-2103, Vol. 141, p. 82-89Article in journal (Refereed)
    Abstract [en]

    We introduce uncertainty regions to perform inference on partial correlations when data are missing not at random. These uncertainty regions are shown to have a desired asymptotic coverage. Their finite sample performance is illustrated via simulations and real data example.

  • 49.
    Gorbach, Tetiana
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Waernbaum, Ingeborg
    Department of Statistics, Uppsala University, Uppsala, Sweden.
    Karvanen, Juha
    Department of Mathematics and Statistics, University of Jyvaskyla, Jyväskylä, Finland.
    Contrasting identifying assumptions of average causal effects: robustness and semiparametric efficiency2023In: Journal of machine learning research, ISSN 1532-4435, E-ISSN 1533-7928, Vol. 24, no 197, p. 1-65Article in journal (Refereed)
    Abstract [en]

    Semiparametric inference on average causal effects from observational data is based on assumptions yielding identification of the effects. In practice, several distinct identifying assumptions may be plausible; an analyst has to make a delicate choice between these models. In this paper, we study three identifying assumptions based on the potential outcome framework:  the back-door assumption, which uses pre-treatment covariates, the front-door assumption, which uses mediators, and the two-door assumption using pre-treatment covariates and mediators simultaneously. We provide the efficient influence functions and the corresponding semiparametric efficiency bounds that hold under these assumptions, and their combinations. We demonstrate that neither of the identification models provides uniformly the most efficient estimation and give conditions under which some bounds are lower than others. We show when semiparametric estimating equation estimators based on influence functions  attain the bounds, and study the robustness of the estimators to misspecification of the nuisance models. The theory is complemented with simulation experiments on the finite sample behavior of the estimators. The results obtained are relevant for an analyst facing a choice between several plausible identifying assumptions and corresponding estimators. Our results show that this choice implies a trade-off between efficiency and robustness to misspecification of the nuisance models. 

    Download full text (pdf)
    fulltext
  • 50.
    Gorbach, Tetiana
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics. Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI). Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB).
    Lundquist, Anders
    Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI). Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Nyberg, Lars
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology. Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB). Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI).
    Salami, Alireza
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology. Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI). Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB). Umeå University, Faculty of Medicine, Wallenberg Centre for Molecular Medicine at Umeå University (WCMM). Aging Research Center, Karolinska Institutet and Stockholm University, Stockholm, Sweden.
    A Hierarchical Bayesian Mixture Model Approach for Analysis of Resting-State Functional Brain Connectivity: An Alternative to Thresholding2020In: Brain Connectivity, ISSN 2158-0014, E-ISSN 2158-0022, Vol. 10, no 5, p. 202-211Article in journal (Refereed)
    Abstract [en]

    This article proposes a Bayesian hierarchical mixture model to analyze functional brain connectivity where mixture components represent "positively connected" and "non-connected" brain regions. Such an approach provides a data-informed separation of reliable and spurious connections in contrast to arbitrary thresholding of a connectivity matrix. The hierarchical structure of the model allows simultaneous inferences for the entire population as well as for each individual subject. A new connectivity measure, the posterior probability of a given pair of brain regions of a specific subject to be connected given the observed correlation of regions' activity, can be computed from the model fit. The posterior probability reflects the connectivity of a pair of regions relative to the overall connectivity pattern of an individual, which is overlooked in traditional correlation analyses. This article demonstrates that using the posterior probability might diminish the effect of spurious connections on inferences, which is present when a correlation is used as a connectivity measure. In addition, simulation analyses reveal that the sparsification of the connectivity matrix using the posterior probabilities might outperform the absolute thresholding based on correlations. Therefore, we suggest that posterior probability might be a beneficial measure of connectivity compared with the correlation. The applicability of the introduced method is exemplified by a study of functional resting-state brain connectivity in older adults.

    Download full text (pdf)
    fulltext
12 1 - 50 of 82
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf