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  • 1.
    Karlsson, Maria
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Analysis of interpurchase intervals of cars using truncated and censored data2005Report (Other academic)
    Abstract [en]

    Knowledge of consumer behaviour is important to companies and governmental authorities for, e.g, planning purposes. In this paper the relationship between interpurchase intervals of  cars and demographic and economic characteristics of the car owners are analysed. Data was collected within a panel study of households in Sweden. Due to the study design, data is left truncated and right censored. The degree of censoring was almost 80 percent which made estimation of the models challenging. Results show that income and age of the car owners are important for the length of the interpurchase intervals of cars. Moreover, the results imply that models recommended for interpurchase intervals of durable goods in general might be inappropriate for interpurchase intervals of cars. A generalized gamma distribution, which embeds several distributions, is used to model interpurchase intervals. Semiparametric models are also considered.

  • 2.
    Karlsson, Maria
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Estimators of regression parameters for truncated and censored data2006In: Metrika, ISSN 1435-926X, Vol. 63, no 3, p. 329-341Article in journal (Refereed)
    Abstract [en]

    Estimators of parameters in semi-parametric left truncated and right censored regression models are proposed. In contrast to the majority of existing estimators, the proposed estimators do not require the error term of the regression model to have a symmetric distribution. In addition the estimators use asymmetric “trimming” of observations. Consistency and asymptotic normality of the estimators are shown. Finite sample properties are considered in a small simulation study. For the left truncated case, an empirical application illustrates the usefulness of the estimator.

  • 3.
    Karlsson, Maria
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Estimators of semiparametric truncated and censored regression models2005Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    This thesis contributes in several ways to the existing knowledge on estimation of truncated, censored, and left truncated right censored (LTRC) regression models. Three new semiparametric estimators are proposed, allowing for asymmetric error distributions. A bootstrap method for estimation of the covariance matrix of the quadratic mode estimator (QME) is proposed and studied. In addition, finite sample properties of estimators for truncated, censored, and LTRC data are studied within simulation studies and applications with real data.

    The first paper consists of a simulation study of the QME and other estimators of truncated regression models. The paper contributes with results suggesting the bootstrap technique being potentially useful for estimation of the QME covariance matrix.

    In the second paper estimators of truncated and censored semiparametric regression models are proposed. These estimators are generalizations of the QME and the winsorized mean estimator (WME) by allowing asymmetric ``trimming'' of observations. Consistency and asymptotic normality of the estimators are shown.

    By combining the two moment restrictions used to derive the estimators in the second paper, a consistent estimator of LTRC regression models is proposed in the third paper.

    The fourth paper contains an application where LTRC interpurchase intervals of cars are analysed. Results regarding the interpurchase behaviour of consumers are provided, as are results on estimator properties.

  • 4.
    Karlsson, Maria
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Finite sample properties of the QME2004In: Communications in statistics. Simulation and computation, ISSN 0361-0918, E-ISSN 1532-4141, Vol. 33, no 3, p. 567-583Article in journal (Refereed)
    Abstract [en]

    Bias and MSE of the QME are studied by means of simulation. A bootstrap estimator of the QME covariance matrix is also included in the study. The simulation is based on travel distances reported in the Swedish Travel Habit Survey. The results are in accordance with the asymptotic properties of the QME. For example, the QME is better than other suggested estimators under asymmetric distributions of the error term. The results also suggest that the bootstrap technique is potentially useful for estimation of the QME covariance matrix.

  • 5.
    Karlsson, Maria
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Omvänt klassrum i en statistikkurs2018In: Digitalisering av högre utbildning / [ed] Stefan Hrastinski, Lund: Studentlitteratur AB, 2018, p. 153-156Chapter in book (Other academic)
  • 6.
    Karlsson, Maria
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Populärvetenskaplig sammanfattning i studentuppsatser: ett sätt att (del-)examineranationellt examensmål nr 4.2015In: Universitetspedagogiska konferensen 2015, Umeå, Umeå universitet, 8-9 okt 2015: Gränslös kunskap, 2015, p. 80-80Conference paper (Other academic)
  • 7.
    Karlsson, Maria
    Umeå University, Faculty of Social Sciences, Statistics.
    Skattning av regressionsparametrar med trunkerade och censurerade data2006In: Quartilen, Vol. 21, no 1, p. 11-14Article in journal (Other academic)
  • 8.
    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.

  • 9.
    Karlsson, Maria
    et al.
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Laitila, Thomas
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    A semiparametric regression estimator under left truncation and right censoring2008In: Statistics and Probability Letters, ISSN 0167-7152, E-ISSN 1879-2103, Vol. 78, no 16, p. 2567-2571Article in journal (Refereed)
    Abstract [en]

    An estimator is proposed for semiparametric linear regression models with left truncated and right censored dependent variables. The estimator is derived from a moment condition following the principles of Newey [Newey, W.K., 2001. Conditional moment restrictions in censored and truncated regression models. Econometric Theory 17, 863–888] on conditional moment conditions. Consistency of the estimator is shown and simulation is used for illustration of the small sample properties.

  • 10.
    Karlsson, Maria
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Laitila, Thomas
    Statistik, Örebro universitet.
    Finite mixture modeling of censored regression models2014In: Statistical papers, ISSN 0932-5026, E-ISSN 1613-9798, Vol. 55, no 3, p. 627-642Article in journal (Refereed)
    Abstract [en]

    A finite mixture of Tobit models is suggested for estimation of regression models with a censored response variable. A mixture of models is not primarily adapted due to a true component structure in the population; the flexibility of the mixture is suggested as a way of avoiding non-robust parametrically specified models. The new estimator has several interesting features. One is its potential to yield valid estimates in cases with a high degree of censoring. The estimator is in a Monte Carlo simulation compared with earlier suggestions of estimators based on semi-parametric censored regression models. Simulation results are partly in favor of the proposed estimator and indicate potentials for further improvements.

  • 11.
    Karlsson, Maria
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Laitila, Thomas
    Using the FMT estimator for analysis of censored household demand data2017In: Royal Statistical Society 2017 International Conference, Glasgow, 4 – 7 september 2017, 2017Conference paper (Other academic)
  • 12.
    Karlsson, Maria
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Lindmark, Anita
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    truncSP: an R package for estimation of semi-parametric truncated linear regression models2014In: Journal of Statistical Software, ISSN 1548-7660, E-ISSN 1548-7660, Vol. 57, no 14, p. 1-19Article in journal (Refereed)
    Abstract [en]

    Problems with truncated data occur in many areas, complicating estimation and inference. Regarding linear regression models, the ordinary least squares estimator is inconsistent and biased for these types of data and is therefore unsuitable for use. Alternative estimators, designed for the estimation of truncated regression models, have been developed. This paper presents the R package truncSP. The package contains functions for the estimation of semi-parametric truncated linear regression models using three different estimators: the symmetrically trimmed least squares, quadratic mode, and left truncated estimators, all of which have been shown to have good asymptotic and finite sample properties. The package also provides functions for the analysis of the estimated models. Data from the environmental sciences are used to illustrate the functions in the package.

  • 13.
    Karlsson, Maria
    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.
    On statistical methods for labor market evaluation under interference between units2016Report (Refereed)
    Abstract [en]

    Evaluation studies aim to provide answers to important questions like: How does this program or policy intervention affect the outcome variables of interest? In order to answer such questions, using the traditional statistical evaluation (or causal inference) methods, some conditions must be satised. One requirement is that the outcomes of individuals are not affected by the treatment given to other individuals, i.e., that the no-interference assumption is satisfied. This assumption might, in many situations, not be plausible. However, recent progress in the research field has provided us with statistical methods for causal inference even under interference. In this paper, we review some of themost important contributions made. We also discuss how we think these methods can or cannot be used within the field of policy evaluation and if there are some measures to be taken when planning an evaluation study in order to be able to use a particular method. In addition, we give examples on how interference has been dealt within some evaluation applications including, but not limited to, labor market evaluations, in the recent past.

  • 14.
    Karlsson, Maria
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Lundquist, Anders
    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.
    Student active learning online and in the classroom by combining the best of Flipped Classroom and MOOCs when teaching statistics2017In: Royal Statistical Society 2017 International Conference, Glasgow, 4-7 september 2017, 2017Conference paper (Other academic)
  • 15.
    Karlsson, Maria
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Lundquist, Anders
    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.
    Student active learning online and in the classroom by combining the best ofFlipped Classroom and MOOCs2017In: Universitetspedagogiska konferensen 2017.: Undervisning i praktiken – föreläsning, flexibelt eller mitt emellan?, 2017, p. 37-37Conference paper (Other academic)
  • 16.
    Karlsson, Maria
    et al.
    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.
    En kurs i kommunikation2019In: Universitetspedagogiska konferensen 2019: Helhetssyn på undervisning - kropp, känsla och kognition i akademin, 2019, p. 24-24Conference paper (Other academic)
  • 17.
    Lee, Myoung-jae
    et al.
    Korea University.
    Karlsson, Maria
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Trimmed and winsorized semiparametric estimator for left-truncated and right-censored regression models2015In: Metrika (Heidelberg), ISSN 0026-1335, E-ISSN 1435-926X, Vol. 78, no 4, p. 485-495Article in journal (Refereed)
    Abstract [en]

    For a linear regression model subject to left-truncation and right-censoring where the truncation and censoring points are known constants (or always observed if random), Karlsson and Laitila (Stat Probab Lett 78:2567–2571,2008) proposed a semiparametric estimator which deals with left-truncation by trimming and right-censoring by ‘winsorizing’. The estimator was motivated by a zero moment condition where a transformed error term appears with trimmed and winsorized tails. This paper takes the semiparametric estimator further by deriving the asymptotic distribution that was not shown in Karlsson and Laitila (Stat Probab Lett 78:2567–2571,2008) and discusses its implementation aspects in practice, albeit brief.

  • 18.
    Lindmark, Anita
    et al.
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    Karlsson, Maria
    Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
    truncSP: Semi-parametric estimators of truncated regression models. R package version 1.12011Other (Other academic)
    Abstract [en]

    An R-package with functions for estimation of semi-parametric linear regression models with truncated response variables (fixed truncation point). Estimation using the Symmetrically Trimmed Least Squares (STLS) estimator (Powell 1986), Quadratic Mode (QME) estimator (Lee 1993) and Left Truncated (LT) estimator (Karlsson 2006).

  • 19.
    Lundin, Mathias
    et al.
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Karlsson, Maria
    Umeå University, Faculty of Social Sciences, Department of Statistics.
    Estimation of causal effects in observational studies with interference between units2014In: Statistical Methods & Applications, ISSN 1618-2510, E-ISSN 1613-981X, Vol. 23, no 3, p. 417-433Article in journal (Refereed)
    Abstract [en]

    Causal effects are usually estimated under the assumption of no interference between individuals. This assumption means that the potential outcomes for one individual are unaffected by the treatments received by other individuals. In many situations, this is not reasonable to assume. Moreover, not taking interference into account could result in misleading conclusions about the effect of a treatment. For two-stage observational studies, where treatment assigment is randomized in the first stage but not in the second stage, we propose IPW estimators of direct and indirect causal effects as defined by Hudgens and Halloran (J Am Stat Assoc 103(482):832-842, 2008) for two-stage randomized studies. We illustrate the use of these estimators in an evaluation study of an implementation of Triple P (a parenting support program) within preschools in Uppsala, Sweden.

1 - 19 of 19
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