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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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    1. Jia Chen & Jiti Gao & Degui Li, 2013. "Estimation in Partially Linear Single-Index Panel Data Models With Fixed Effects," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(3), pages 315-330, July.
    2. Hu Yang & Chaohui Guo & Jing Lv, 2016. "Variable selection for generalized varying coefficient models with longitudinal data," Statistical Papers, Springer, vol. 57(1), pages 115-132, March.
    3. José Lombardía, María & Sperlich, Stefan, 2012. "A new class of semi-mixed effects models and its application in small area estimation," Computational Statistics & Data Analysis, Elsevier, vol. 56(10), pages 2903-2917.
    4. Su, Liangjun & Ullah, Aman, 2006. "Profile likelihood estimation of partially linear panel data models with fixed effects," Economics Letters, Elsevier, vol. 92(1), pages 75-81, July.
    5. Jiming Jiang & P. Lahiri, 2006. "Mixed model prediction and small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 15(1), pages 1-96, June.
    6. Lam, Clifford & Fan, Jianqing, 2008. "Profile-kernel likelihood inference with diverging number of parameters," LSE Research Online Documents on Economics 31548, London School of Economics and Political Science, LSE Library.
    7. Hua Liang, 2009. "Generalized partially linear mixed-effects models incorporating mismeasured covariates," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 61(1), pages 27-46, March.
    8. María José Lombardía & Stefan Sperlich, 2008. "Semiparametric inference in generalized mixed effects models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 913-930, November.
    9. Rodríguez-Póo, Juan M. & Sperlich, Stefan & Vieu, Philippe, 2003. "Semiparametric Estimation Of Separable Models With Possibly Limited Dependent Variables," Econometric Theory, Cambridge University Press, vol. 19(6), pages 1008-1039, December.
    10. Daowen Zhang, 2004. "Generalized Linear Mixed Models with Varying Coefficients for Longitudinal Data," Biometrics, The International Biometric Society, vol. 60(1), pages 8-15, March.
    11. X. Lin & D. Zhang, 1999. "Inference in generalized additive mixed modelsby using smoothing splines," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(2), pages 381-400, April.
    12. J. D. Opsomer & G. Claeskens & M. G. Ranalli & G. Kauermann & F. J. Breidt, 2008. "Non‐parametric small area estimation using penalized spline regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 265-286, February.
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