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  • 51.
    Häggström, Jenny
    et al.
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Estimating prediction error: cross-validation vs. accumulated prediction error2010In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 39, no 5, p. 880-898Article in journal (Refereed)
    Abstract [en]

    We study the validation of prediction rules such as regression models and classification algorithms through two out-of-sample strategies, cross-validation and accumulated prediction error. We use the framework of Efron (1983) where measures of prediction errors are defined as sample averages of expected errors and show through exact finite sample calculations that cross-validation and accumulated prediction error yield different smoothing parameter choices in nonparametric regression. The difference in choice does not vanish as sample size increases.

  • 52.
    Häggström, Jenny
    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.
    Targeted smoothing parameter selection for estimating average causal effects2014In: Computational statistics (Zeitschrift), ISSN 0943-4062, E-ISSN 1613-9658, Vol. 29, no 6, p. 1727-1748Article in journal (Refereed)
    Abstract [en]

    The non-parametric estimation of average causal effects in observational studies often relies on controlling for confounding covariates through smoothing regression methods such as kernel, splines or local polynomial regression. Such regression methods are tuned via smoothing parameters which regulates the amount of degrees of freedom used in the fit. In this paper we propose data-driven methods for selecting smoothing parameters when the targeted parameter is an average causal effect. For this purpose, we propose to estimate the exact expression of the mean squared error of the estimators. Asymptotic approximations indicate that the smoothing parameters minimizing this mean squared error converges to zero faster than the optimal smoothing parameter for the estimation of the regression functions. In a simulation study we show that the proposed data-driven methods for selecting the smoothing parameters yield lower empirical mean squared error than other methods available such as, e.g., cross-validation.

  • 53.
    Häggström, Jenny
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Persson, Emma
    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.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    CovSel: An R Package for Covariate Selection When Estimating Average Causal Effects2015In: Journal of Statistical Software, ISSN 1548-7660, E-ISSN 1548-7660, Vol. 68, no 1, p. 1-20Article in journal (Refereed)
    Abstract [en]

    We describe the R package CovSel, which reduces the dimension of the covariate vector for the purpose of estimating an average causal effect under the unconfoundedness assumption. Covariate selection algorithms developed in De Luna, Waernbaum, and Richardson (2011) are implemented using model-free backward elimination. We show how to use the package to select minimal sets of covariates. The package can be used with continuous and discrete covariates and the user can choose between marginal co-ordinate hypothesis tests and kernel-based smoothing as model-free dimension reduction techniques.

  • 54.
    Häggström, Jenny
    et al.
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Westerlund, Olle
    Umeå University, Faculty of Social Sciences, Department of Economics.
    Norberg, Margareta
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Epidemiology and Global Health.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Divorcing in middle age and its effects on BMIManuscript (preprint) (Other academic)
  • 55.
    Ivarsson, Anneli
    et al.
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Epidemiology and Global Health.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Nilsson, Karina
    Umeå University, Faculty of Social Sciences, Department of Sociology.
    Lindgren, Urban
    Umeå University, Faculty of Social Sciences, Department of Social and Economic Geography.
    Bergdahl, Ingvar
    Umeå University, Faculty of Medicine, Department of Public Health and Clinical Medicine, Occupational and Environmental Medicine.
    Registerdata om barndomen: kunskapsbas för hållbar hälsa och välfärd2010In: SVEPET. Medlemsbladet för Svensk Epidemiologisk Förening (Svep), Vol. 28, no 3, p. 4-6Article in journal (Other academic)
  • 56.
    Josefsson, Maria
    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.
    Daniels, Michael
    University of Texas at Austin.
    Nyberg, Lars
    Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI). Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology. Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB), Physiology.
    Causal inference with longitudinal outcomes and non-ignorable drop-out: Estimating the effect of living alone on cognitive decline2016In: Journal of the Royal Statistic Society, Series C: Applied Statistics, ISSN 0035-9254, E-ISSN 1467-9876, Vol. 65, no 1, p. 131-144Article in journal (Refereed)
    Abstract [en]

    We develop a model to estimate the causal effect of living arrangement (living alone versus living with someone) on cognitive decline based on a 15-year prospective cohort study, where episodic memory function is measured every 5 years. One key feature of the model is the combination of propensity score matching to balance confounding variables between the two living arrangement groups—to reduce bias due to unbalanced covariates at baseline, with a pattern–mixture model for longitudinal data—to deal with non-ignorable dropout. A fully Bayesian approach allows us to convey the uncertainty in the estimation of the propensity score and subsequent matching in the inference of the causal effect of interest. The analysis conducted adds to previous studies in the literature concerning the protective effect of living with someone, by proposing a modelling approach treating living arrangement as an exposure.

  • 57.
    Josefsson, Maria
    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).
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Pudas, Sara
    Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI).
    Nilsson, Lars-Göran
    Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI). Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB).
    Nyberg, Lars
    Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI). Umeå University, Faculty of Social Sciences, Centre for Population Studies (CPS). Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB), Physiology.
    Genetic and lifestyle predictors of 15-Year longitudinal change in episodic memory2012In: Journal of The American Geriatrics Society, ISSN 0002-8614, E-ISSN 1532-5415, Vol. 60, no 12, p. 2308-2312Article in journal (Refereed)
    Abstract [en]

    OBJECTIVES: To reveal distinct longitudinal trajectories in episodic memory over 15 years and to identify demographic, lifestyle, health-related, and genetic predictors of stability or decline. DESIGN: Prospective cohort study. SETTING: The Betula Project, Umeå, Sweden. PARTICIPANTS: One thousand nine hundred fifty-four healthy participants aged 35 to 85 at baseline. MEASUREMENTS: Memory was assessed according to validated episodic memory tasks in participants from a large population-based sample. Data were analyzed using a random-effects pattern-mixture model that considered the effect of attrition over two to four longitudinal sessions. Logistic regression was used to determine significant predictors of stability or decline relative to average change in episodic memory. RESULTS: Of 1,558 participants with two or more test sessions, 18% were classified as maintainers and 13% as decliners, and 68% showed age-typical average change. More educated and more physically active participants, women, and those living with someone were more likely to be classified as maintainers, as were carriers of the met allele of the catechol-O-methyltransferase gene. Less educated participants, those not active in the labor force, and men were more likely to be classified as decliners, and the apolipoprotein E ɛ4 allele was more frequent in decliners. CONCLUSION: Quantitative, attrition-corrected assessment of longitudinal changes in memory can reveal substantial heterogeneity in aging trajectories, and genetic and lifestyle factors predict such heterogeneity.

  • 58.
    Karlsson, Maria
    et al.
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Cantoni, Eva
    University of Geneva, Department of Econometrics.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Local polynomial regression with truncated or censored response2009Report (Other academic)
    Abstract [en]

    Truncation or censoring of the response variable in a regression model is a problem in many applications, e.g. when the response is insurance claims or the durations of unemployment spells. We introduce a local polynomial re­gression estimator which can deal with such truncated or censored responses. For this purpose, we use local versions of the STLS and SCLS estimators of Powell (1986) and the QME estimator of Lee (1993) and Laitila (2001). The asymptotic properties of our estimators, and the conditions under which they are valid, are given. In addition, a simulation study is presented to investigate the finite sample properties of our proposals.

  • 59.
    Lindgren, Urban
    et al.
    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.
    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.
    Data Resource Profile: Swedish Microdata Research from Childhood into Lifelong Health and Welfare (Umeå SIMSAM Lab)2016In: International Journal of Epidemiology, ISSN 0300-5771, E-ISSN 1464-3685, Vol. 45, no 4, p. 1075-1075gArticle in journal (Refereed)
  • 60.
    Lindmark, Anita
    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.
    Eriksson, Marie
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Sensitivity analysis for unobserved confounding of direct and indirect effects using uncertainty intervals2018In: Statistics in Medicine, ISSN 0277-6715, E-ISSN 1097-0258, Vol. 37, no 10, p. 1744-1762Article in journal (Refereed)
    Abstract [en]

    To estimate direct and indirect effects of an exposure on an outcome from observed data, strong assumptions about unconfoundedness are required. Since these assumptions cannot be tested using the observed data, a mediation analysis should always be accompanied by a sensitivity analysis of the resulting estimates. In this article, we propose a sensitivity analysis method for parametric estimation of direct and indirect effects when the exposure, mediator, and outcome are all binary. The sensitivity parameters consist of the correlations between the error terms of the exposure, mediator, and outcome models. These correlations are incorporated into the estimation of the model parameters and identification sets are then obtained for the direct and indirect effects for a range of plausible correlation values. We take the sampling variability into account through the construction of uncertainty intervals. The proposed method is able to assess sensitivity to both mediator‐outcome confounding and confounding involving the exposure. To illustrate the method, we apply it to a mediation study based on the data from the Swedish Stroke Register (Riksstroke). An R package that implements the proposed method is available.

  • 61.
    Persson, Emma
    et al.
    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.
    Waernbaum, Ingeborg
    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.
    Data-driven algorithms for dimension reduction in causal inference2017In: Computational Statistics & Data Analysis, ISSN 0167-9473, E-ISSN 1872-7352, Vol. 105, p. 280-292Article in journal (Refereed)
    Abstract [en]

    In observational studies, the causal effect of a treatment may be confounded with variables that are related to both the treatment and the outcome of interest. In order to identify a causal effect, such studies often rely on the unconfoundedness assumption, i.e., that all confounding variables are observed. The choice of covariates to control for, which is primarily based on subject matter knowledge, may result in a large covariate vector in the attempt to ensure that unconfoundedness holds. However, including redundant covariates can affect bias and efficiency of nonparametric causal effect estimators, e.g., due to the curse of dimensionality. In this paper, data-driven algo- rithms for the selection of sufficient covariate subsets are investigated. Under the assumption of unconfoundedness we search for minimal subsets of the covariate vector. Based on the framework of sufficient dimension reduction or kernel smoothing, the algorithms perform a backward elim- ination procedure testing the significance of each covariate. Their performance is evaluated in simulations and an application using data from the Swedish Childhood Diabetes Register is also presented.

  • 62.
    Pudas, Sara
    et al.
    Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB), Physiology. Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI).
    Persson, Jonas
    Umeå University, Faculty of Medicine, Umeå Centre for Functional Brain Imaging (UFBI).
    Josefsson, Maria
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Nilsson, Lars-Göran
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Diagnostic Radiology.
    Nyberg, Lars
    Umeå University, Faculty of Medicine, Department of Integrative Medical Biology (IMB), Physiology. Umeå University, Faculty of Medicine, Department of Radiation Sciences.
    Brain Characteristics of Individuals Resisting Age-Related Cognitive Decline over Two Decades2013In: Journal of Neuroscience, ISSN 0270-6474, E-ISSN 1529-2401, Vol. 33, no 20, p. 8668-8677Article in journal (Refereed)
    Abstract [en]

    Some elderly appear to resist age-related decline in cognitive functions, but the neural correlates of successful cognitive aging are not well known. Here, older human participants from a longitudinal study were classified as successful or average relative to the mean attrition-corrected cognitive development across 15-20 years in a population-based sample (n = 1561). Fifty-one successful elderly and 51 age-matched average elderly (mean age: 68.8 years) underwent functional magnetic resonance imaging while performing an episodic memory face-name paired-associates task. Successful older participants had higher BOLD signal during encoding than average participants, notably in the bilateral PFC and the left hippocampus (HC). The HC activation of the average, but not the successful, older group was lower than that of a young reference group (n = 45, mean age: 35.3 years). HC activation was correlated with task performance, thus likely contributing to the superior memory performance of successful older participants. The frontal BOLD response pattern might reflect individual differences present from young age. Additional analyses confirmed that both the initial cognitive level and the slope of cognitive change across the longitudinal measurement period contributed to the observed group differences in BOLD signal. Further, the differences between the older groups could not be accounted for by differences in brain structure. The current results suggest that one mechanism behind successful cognitive aging might be preservation of HC function combined with a high frontal responsivity. These findings highlight sources for heterogeneity in cognitive aging and may hold useful information for cognitive intervention studies.

  • 63.
    Stenberg, Anders
    et al.
    University of Stockholm.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Westerlund, Olle
    Umeå University, Faculty of Social Sciences, Department of Economics.
    Can adult education delay retirement from the Labour Market?2012In: Journal of Population Economics, ISSN 0933-1433, E-ISSN 1432-1475, ISSN 0933-1433, Vol. 25, no 2, p. 677-696Article in journal (Refereed)
    Abstract [en]

    We examine whether adult education delays retirement to potentially increase labour force participation among the elderly, a mechanism suggested in the OECD strategy for “active ageing” and the “Lisbon strategy” of the EU. Using register data from Sweden, we analyse transcripts from adult education for the period 1979–2004 and annual earnings 1982–2004. We match samples of treated individuals, in adult education 1986–1989, and untreated on the propensity score. The timing of exit from the workforce is assessed by non-parametric estimation of survival rates in the labour force. The results indicate no effects of adult education on the timing of retirement.

  • 64.
    Stenberg, Anders
    et al.
    University of Stockholm.
    de Luna, Xavier
    Umeå University, Faculty of Social Sciences, Department of Statistics. Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics. IFAU, Uppsala, Sweden.
    Westerlund, Olle
    Umeå University, Faculty of Social Sciences, Department of Economics. Jyväskylä University School of Business and Economics, Jyväskylä, Finland.
    Does formal education for older workers increase earnings?: evidence based on rich data and long-term follow up2014In: Labour, ISSN 1121-7081, E-ISSN 1467-9914, Vol. 28, no 2, p. 163-189Article in journal (Refereed)
    Abstract [en]

    Governments in Europe, Canada and the US have expressed an ambition to stimulate education of older. In this paper, we analyze if there are effects on annual earnings of formal education for participants aged 42-55 at the time of enrolment in 1994-1995. The analysis explores longitudinal population register data stretching from 1982 to 2007. The method used is difference-in-differences propensity score matching based on a rich set of covariates, including indicators of health and labor market marginalization. Our findings underline the importance of long follow up periods and imply positive effects for females, especially so for women with children, and no significant average earnings effects for males. These results differ from earlier studies but are stable to several alternative assumptions regarding unobservable characteristics. Data further indicate that the gender gap in our estimates may stem from differences in underlying reasons for enrolment.

  • 65.
    Svensson, Ingrid
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics. Umeå University, Faculty of Social Sciences, Centre for Demographic and Ageing Research (CEDAR).
    Lundholm, Emma
    Umeå University, Faculty of Social Sciences, Department of Geography and Economic History, Economic and social geography. 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. Umeå University, Faculty of Social Sciences, Centre for Demographic and Ageing Research (CEDAR).
    Malmbeg, Gunnar
    Umeå University, Faculty of Social Sciences, Department of Geography and Economic History, Economic and social geography. Umeå University, Faculty of Social Sciences, Centre for Demographic and Ageing Research (CEDAR).
    Family Life Course and the Timing of Women's Retirement: a Sequence Analysis Approach2015In: Population, Space and Place, ISSN 1544-8444, E-ISSN 1544-8452, Vol. 21, no 8, p. 856-871Article in journal (Refereed)
    Abstract [en]

    Based on longitudinal data from national Swedish registers, family life courses dynamics for all women born 1935 in Sweden are explored for the period 1990-2006. Focusing primarily on the existence and geographical proximity to parents, children and grandchildren, assuming that the family life courses affect the life situation as well as strategic decisions, this longitudinal study uses a holistic approach, analysing how different types of family life courses are associated with socio-economic conditions as well as with the timing of retirement. The primary task was not to identify the causal determinants of work life exit, but rather to unfold how retirement transition is entwined into the different types of family life courses, whereby retirement and family ageing are different sides of a multifaceted transition period. By using sequence analysis, the family life courses were structured into sequences and durations of states and different family life course categories were identified.

    The sequence analyses reveal a complex relation between retirement decisions and having family members around. Early retirement was associated with a category with few relatives but also with a category with two younger generations present, while we found no strong association with early retirement for categories in which the old generation was around for a longer period. Late retirement was associated with belonging to categories characterized by late family formation and having children at home. These differences in retirement behaviour were also significant when controlling for education level, marital status and type of region in a Cox regression.

  • 66.
    Wijayatunga, Priyantha
    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.
    A Consistency Result for Bayes Classifiers with Censored Response Data2013In: Theoretical mathematics and applications, ISSN 1792-9709, Vol. 3, no 4, p. 47-54Article in journal (Refereed)
    Abstract [en]

    Naive Bayes classifiers have proven to be useful in many prediction problems with complete training data. Here we consider the situation where a naive Bayes classifier is trained with data where the response is right censored. Such prediction problems are for instance encountered in profiling systems used at National Employment Agencies. In this paper we propose the maximum collective conditional likelihood estimator for the prediction and show that it is strongly consistent under the usual identifiability condition.

  • 67.
    Xavier, de Luna
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Lundin, Mathias
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Sensitivity analysis of the unconfoundedness assumption with an application to an evaluation of college choice effects on earnings2014In: Journal of Applied Statistics, ISSN 0266-4763, E-ISSN 1360-0532, Vol. 41, no 8, p. 1767-1784Article in journal (Refereed)
    Abstract [en]

    We evaluate the effects of college choice on earnings using Swedish register databases. This case study is used to motivate the introduction of a novel procedure to analyse the sensitivity of such an observational study to the assumption made that there are no unobserved confounders – variables affecting both college choice and earnings. This assumption is not testable without further information, and should be considered an approximation of reality. To perform a sensitivity analysis, we measure the departure from the unconfoundedness assumption with the correlation between college choice and earnings when conditioning on observed covariates. The use of a correlation as a measure of dependence allows us to propose a standardised procedure by advocating the use of a fixed value for the correlation, typically 1% or 5%, when checking the sensitivity of an evaluation study. A correlation coefficient is, moreover, intuitive to most empirical scientists, which makes the results of our sensitivity analysis easier to communicate than those of previously proposed methods. In our evaluation of the effects of college choice on earnings, the significantly positive effect obtained could not be questioned by a sensitivity analysis allowing for unobserved confounders inducing at most 5% correlation between college choice and earnings.

12 51 - 67 of 67
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