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Inference for longitudinal data from complex sampling surveys: An approach based on quadratic inference functions

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  • Laura Dumitrescu
  • Wei Qian
  • J. N. K. Rao

Abstract

We propose a survey weighted quadratic inference function method for the analysis of data collected from longitudinal surveys, as an alternative to the survey weighted generalized estimating equation method. The procedure yields estimators of model parameters, which are shown to be consistent and have a limiting normal distribution. Furthermore, based on the inference function, a pseudolikelihood ratio type statistic for testing a composite hypothesis on model parameters and a statistic for testing the goodness of fit of the assumed model are proposed. We establish their asymptotic distributions as weighted sums of independent chi‐squared random variables and obtain Rao‐Scott corrections to those statistics leading to a chi‐squared distribution, approximately. We examine the performance of the proposed methods in a simulation study.

Suggested Citation

  • Laura Dumitrescu & Wei Qian & J. N. K. Rao, 2021. "Inference for longitudinal data from complex sampling surveys: An approach based on quadratic inference functions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(1), pages 246-274, March.
    • Handle: RePEc:bla:scjsta:v:48:y:2021:i:1:p:246-274
    DOI: 10.1111/sjos.12448
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    5. F. Jay Breidt & Jean D. Opsomer & Ismael Sanchez-Borrego, 2016. "Nonparametric Variance Estimation Under Fine Stratification: An Alternative to Collapsed Strata," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 822-833, April.
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