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Missing data arise commonly in applications, and research on this topic has received extensive attention in the past few decades. Various inference methods have been developed under different missing data mechanisms, including missing at random and missing not at random. The assessment of a feasible missing data mechanism is, however, difficult due to the lack of validation data. The problem is further complicated by the presence of spurious variables in covariates. Focusing on missingness in the response variable, a unified modeling scheme is proposed by utilizing the parametric generalized additive model to characterize various types of missing data processes. Taking the generalized linear model to facilitate the dependence of the response on the associated covariates, the concurrent estimation and variable selection procedures are developed using regularized likelihood, and the asymptotic properties for the resultant estimators are rigorously established. The proposed methods are appealing in their flexibility and generality; they circumvent the need of assuming a particular missing data mechanism that is required by most available methods. Empirical studies demonstrate that the proposed methods result in satisfactory performance in finite sample settings. Extensions to accommodating missingness in both the response and covariates are also discussed. [ABSTRACT FROM AUTHOR]

This study uses Robert Cummins's Personal Wellbeing Index (PWI) to see how seven different aspects of personal life impact the life satisfaction and well-being of young adults living in areas undergoing structural change. The study has its background in evidence from earlier research suggesting a polarization of well-being among young people. The methodological approaches applied are quantile regression and a generalized additive model, which are used to examine components of subjective well-being in different life satisfaction groups. The results show that well-being is differentially constructed. At the extreme pole of ill-being, the primary causes of life dissatisfaction are future outlook, standard of living, health and community. At the opposite pole of well-being, the main factors contributing to life satisfaction are future outlook, safety, health and standard of living. In addition, a more detailed analysis of the young people in the ill-being group revealed a phenomenon that further perpetuates marginalization: that is, the relationship between social relations and performance components is inverse to life satisfaction at the ill-being pole. [ABSTRACT FROM AUTHOR]

Abstract: The problem of variable selection within the class of generalized additive models, when there are many covariates to choose from but the number of predictors is still somewhat smaller than the number of observations, is considered. Two very simple but effective shrinkage methods and an extension of the nonnegative garrote estimator are introduced. The proposals avoid having to use nonparametric testing methods for which there is no general reliable distributional theory. Moreover, component selection is carried out in one single step as opposed to many selection procedures which involve an exhaustive search of all possible models. The empirical performance of the proposed methods is compared to that of some available techniques via an extensive simulation study. The results show under which conditions one method can be preferred over another, hence providing applied researchers with some practical guidelines. The procedures are also illustrated analysing data on plasma beta-carotene levels from a cross-sectional study conducted in the United States. [Copyright &y& Elsevier]

Abstract: Generalized additive models (GAMs) have become the standard tool for the analysis of short-term effects of air pollution on human health. Usually, the confounding effect of seasonality and long-term trend is described by flexible parametric or non-parametric functions of calendar time. Two different modeling strategies, i.e. GAM with penalized regression splines and GAM with regression splines, were compared by means of a simulation study, addressing attention to the inference on air pollutant effect. Simulation results indicated that GAM with regression splines provides negligibly biased estimates of air pollutant effect and it is robust to misspecification of the degrees of freedom of the spline. GAM with penalized regression splines requires a certain amount of undersmoothing in order to reduce the bias of the estimates and to improve the coverage of confidence intervals. These findings agree with asymptotic results developed in the context of partially splined models. [Copyright &y& Elsevier]

A flexible method for modelling capture–recapture data with continuous covariates that describe heterogeneous catchability is developed. The well established generalized additive modelling framework is used. An estimator of population size is developed using this method. The performance of the method is demonstrated using neural tube defect capture–recapture data from the Netherlands, with the birth weight of a child as a covariate. The parametric bootstrap is used for variance estimation. [Copyright &y& Elsevier]