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

Hiroshi Shiraishi, Masanobu Taniguchi and Takashi Yamashita

Journal of Multivariate Analysis, 2018, vol. 166, issue C, 198-211

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 dimuKeywords: Curved statistical model; Dependent data; Higher-order asymptotic theory; Maximum likelihood estimation; Portfolio estimation; Regression model; Shrinkage estimator; Stationary process (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jmvana:v:166:y:2018:i:c:p:198-211

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DOI: 10.1016/j.jmva.2018.03.006

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