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Semi-parametric small area inference in generalized semi-varying coefficient mixed effects models

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  • Xuemei Hu

    (Chongqing Technology and Business University
    Chinese Academy of Sciences)

  • Weiming Yang

    (Chongqing Technology and Business University)

Abstract

We investigate semi-parametric small area inference in generalized semi-varying coefficient mixed effects models with application to longitudinal data. Combining the generalized profiled likelihood approaches for mixed effect models with kernel methods, we not only construct semi-parametric small area estimators, but also propose two test statistics for discriminating between a parametric mixed effects model and a generalized semi-varying coefficient mixed effects model. The critical values are estimated by a bootstrap procedure. The asymptotic theory for the methods is provided. Simulations exhibit the finite-sample performance for the proposed estimators and test statistics. These verify the feasibility and the excellent behavior of the methods for moderate sample sizes.

Suggested Citation

  • Xuemei Hu & Weiming Yang, 2019. "Semi-parametric small area inference in generalized semi-varying coefficient mixed effects models," Statistical Papers, Springer, vol. 60(4), pages 1039-1058, August.
    • Handle: RePEc:spr:stpapr:v:60:y:2019:i:4:d:10.1007_s00362-016-0862-8
    DOI: 10.1007/s00362-016-0862-8
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    References listed on IDEAS

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