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Empirical Likelihood Inference Under Stratified Random Sampling in the Presence of Measurement Error

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Abstract

Suppose that several different imperfect instruments and one perfect instrument are used independently to measure some characteristic of a population. In order to make full use of the sample information, in this paper the empirical likelihood method is put forward for making inferences on parameters of interest under stratified random sampling in the presence of measurement error. Our results show that it can lead to estimators which are asymptotically normal and utilize all the available sample information. We also obtain the asymptotic distribution of empirical likelihood testing statistics. In particular, we apply the method to obtain estimator and confidence interval of population mean.

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Authors and Affiliations

  1. Department of Statistics, School of Mathematics, Nankai University, Tianjin, 300071, China

    Chang-chun Wu & Run-chu Zhang

  2. Department of Mathematics, Xiangnan University, Chenzhou, 423000, China

    Chang-chun Wu

Authors
  1. Chang-chun Wu
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  2. Run-chu Zhang
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Correspondence to Chang-chun Wu.

Additional information

Supported by the National Natural Science Foundation of China (No.10171051)

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Wu, Cc., Zhang, Rc. Empirical Likelihood Inference Under Stratified Random Sampling in the Presence of Measurement Error. Acta Mathematicae Applicatae Sinica, English Series 21, 429–440 (2005). https://doi.org/10.1007/s10255-005-0250-y

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  • DOI: https://doi.org/10.1007/s10255-005-0250-y

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