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Modelling complex survey data with population level information: an empirical likelihood approach

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  • M. Oguz-Alper
  • Y. G. Berger

Abstract

Survey data are often collected with unequal probabilities from a stratified population. In many modelling situations, the parameter of interest is a subset of a set of parameters, with the others treated as nuisance parameters. We show that in this situation the empirical likelihood ratio statistic follows a chi-squared distribution asymptotically, under stratified single and multi-stage unequal probability sampling, with negligible sampling fractions. Simulation studies show that the empirical likelihood confidence interval may achieve better coverages and has more balanced tail error rates than standard approaches involving variance estimation, linearization or resampling.

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  • M. Oguz-Alper & Y. G. Berger, 2016. "Modelling complex survey data with population level information: an empirical likelihood approach," Biometrika, Biometrika Trust, vol. 103(2), pages 447-459.
    • Handle: RePEc:oup:biomet:v:103:y:2016:i:2:p:447-459.
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    File URL: http://hdl.handle.net/10.1093/biomet/asw014
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    Cited by:

    1. Hanson, Timothy E. & de Carvalho, Miguel & Chen, Yuhui, 2017. "Bernstein polynomial angular densities of multivariate extreme value distributions," Statistics & Probability Letters, Elsevier, vol. 128(C), pages 60-66.

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