Linear shrinkage estimation of the variance of a distribution with unknown mean

Yuki Ikeda, Ryumei Nakada, Tatsuya Kubokawa and Muni S. Srivastava

Communications in Statistics - Theory and Methods, 2021, vol. 50, issue 9, 2039-2047

Abstract: In estimation of the variance of a distribution with unknown mean, the paper suggests the linear shrinkage estimators motivated from a Bayesian perspective. The so-called Stein’s truncated estimator of the variance can be derived as the linear shrinkage estimator when the distribution is normal. The method of the linear shrinkage estimation is extended to non normal distributions and to linear regression models. The linear shrinkage estimator with the optimal weight estimate, derived without assuming normality, is shown to have a good numerical performance for several distributions.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:50:y:2021:i:9:p:2039-2047

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DOI: 10.1080/03610926.2019.1657457

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