115 research outputs found

    Bayesian Semiparametric Hierarchical Empirical Likelihood Spatial Models

    Full text link
    We introduce a general hierarchical Bayesian framework that incorporates a flexible nonparametric data model specification through the use of empirical likelihood methodology, which we term semiparametric hierarchical empirical likelihood (SHEL) models. Although general dependence structures can be readily accommodated, we focus on spatial modeling, a relatively underdeveloped area in the empirical likelihood literature. Importantly, the models we develop naturally accommodate spatial association on irregular lattices and irregularly spaced point-referenced data. We illustrate our proposed framework by means of a simulation study and through three real data examples. First, we develop a spatial Fay-Herriot model in the SHEL framework and apply it to the problem of small area estimation in the American Community Survey. Next, we illustrate the SHEL model in the context of areal data (on an irregular lattice) through the North Carolina sudden infant death syndrome (SIDS) dataset. Finally, we analyze a point-referenced dataset from the North American Breeding Bird survey that considers dove counts for the state of Missouri. In all cases, we demonstrate superior performance of our model, in terms of mean squared prediction error, over standard parametric analyses.Comment: 29 pages, 3 figue

    Estimating regional unemployment with mobile network data for Functional Urban Areas in Germany

    Get PDF
    The ongoing growth of cities due to better job opportunities is leading to increased labour-relatedcommuter flows in several countries. On the one hand, an increasing number of people commuteand move to the cities, but on the other hand, the labour market indicates higher unemployment ratesin urban areas than in the surrounding areas. We investigate this phenomenon on regional level byan alternative definition of unemployment rates in which commuting behaviour is integrated. Wecombine data from the Labour Force Survey (LFS) with dynamic mobile network data by small areamodels for the federal state North Rhine-Westphalia in Germany. From a methodical perspective, weuse a transformed Fay-Herriot model with bias correction for the estimation of unemployment ratesand propose a parametric bootstrap for the Mean Squared Error (MSE) estimation that includes thebias correction. The performance of the proposed methodology is evaluated in a case study based onofficial data and in model-based simulations. The results in the application show that unemploymentrates (adjusted by commuters) in German cities are lower than traditional official unemployment ratesindicate

    Robust small area estimation

    Get PDF
    Small area estimation has long been a popular and important research topic in survey statistics. For the basic area level model, popularly known as Fay-Herriot model, we first make inference without any distributional assumptions with the exception of a few moment assumptions. In the process, we propose a new method of model parameter estimation, study its statistical properties and use the resulting parameter estimators as components in small area estimators. The second order approximation of the mean squared error of the proposed small area estimators is derived, and we also describe a second order correct estimator of the mean squared error. Then we develop confidence intervals for the small area parameters that are second order correct under normal distributional assumptions. For the unit level model, popularly known as nested-error regression model, we introduce a model-based design consistent estimator for a finite population domain mean

    Simultaneous inference for linear mixed model parameters with an application to small area estimation

    Get PDF
    Open access financiado por Universite de Geneve (article funding)European Regional Development Fund[Abstract]: Over the past decades, linear mixed models have attracted considerable attention in various fields of applied statistics. They are popular whenever clustered, hierarchical or longitudinal data are investigated. Nonetheless, statistical tools for valid simultaneous inference for mixed parameters are rare. This is surprising because one often faces inferential problems beyond the pointwise examination of fixed or mixed parameters. For example, there is an interest in a comparative analysis of cluster-level parameters or subject-specific estimates in studies with repeated measurements. We discuss methods for simultaneous inference assuming a linear mixed model. Specifically, we develop simultaneous prediction intervals as well as multiple testing procedures for mixed parameters. They are useful for joint considerations or comparisons of cluster-level parameters. We employ a consistent bootstrap approximation of the distribution of max-type statistic to construct our tools. The numerical performance of the developed methodology is studied in simulation experiments and illustrated in a data example on household incomes in small areas.Swiss National Science Foundation; 200021-192345,Swiss National Science Foundation; P2GEP2_195898Xunta de Galicia; ED431C 2020/14Ministerio de Ciencia e Innovación; PID2020-113578RB-I00Galician Innovation Agency/ Ministerio de Economía, empleo e industria; COV20/00604Xunta de Galicia; ED431G2019/0
    • …
    corecore