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Higher-order asymptotic theory of shrinkage estimation for general statistical models

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  • Shiraishi, Hiroshi
  • Taniguchi, Masanobu
  • Yamashita, Takashi

Abstract

In this study, we develop a higher-order asymptotic theory of shrinkage estimation for general statistical models, which includes dependent processes, multivariate models, and regression models (i.e., non-independent and identically distributed models). We introduce a shrinkage estimator of the maximum likelihood estimator (MLE) and compare it with the standard MLE by using the third-order mean squared error. A sufficient condition for the shrinkage estimator to improve the MLE is given in a general setting. Our model is described as a curved statistical model p(⋅;θ(u)), where θ is a parameter of the larger model and u is a parameter of interest with dimu

Suggested Citation

  • Shiraishi, Hiroshi & Taniguchi, Masanobu & Yamashita, Takashi, 2018. "Higher-order asymptotic theory of shrinkage estimation for general statistical models," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 198-211.
    • Handle: RePEc:eee:jmvana:v:166:y:2018:i:c:p:198-211
    DOI: 10.1016/j.jmva.2018.03.006
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    References listed on IDEAS

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