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Measurement Error Models for Replicated Data Under Asymmetric Heavy-Tailed Distributions

Author

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  • Chunzheng Cao

    (Nanjing University of Information Science and Technology
    Seoul National University)

  • Yahui Wang

    (Nanjing University of Information Science and Technology)

  • Jian Qing Shi

    (University of Newcastle)

  • Jinguan Lin

    (Nanjing Audit University)

Abstract

Replicated data with measurement errors are frequently presented in economical, environmental, chemical, medical and other fields. In this paper, we discuss a replicated measurement error model under the class of scale mixtures of skew-normal distributions, which extends symmetric heavy and light tailed distributions to asymmetric cases. We also consider equation error in the model for displaying the matching degree between the true covariate and response. Explicit iterative expressions of maximum likelihood estimates are provided via the expectation–maximization type algorithm. Empirical Bayes estimates are conducted for predicting the true covariate and response. We study the effectiveness as well as the robustness of the maximum likelihood estimations through two simulation studies. The method is applied to analyze a continuing survey data of food intakes by individuals on diet habits.

Suggested Citation

  • Chunzheng Cao & Yahui Wang & Jian Qing Shi & Jinguan Lin, 2018. "Measurement Error Models for Replicated Data Under Asymmetric Heavy-Tailed Distributions," Computational Economics, Springer;Society for Computational Economics, vol. 52(2), pages 531-553, August.
    • Handle: RePEc:kap:compec:v:52:y:2018:i:2:d:10.1007_s10614-017-9702-8
    DOI: 10.1007/s10614-017-9702-8
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