Covariance matrix estimation in a seemingly unrelated regression model under Stein’s loss
| Autor: | Hiroshi Kurata, Shun Matsuura |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
Covariance matrix Estimator Regression analysis Function (mathematics) Seemingly unrelated regressions 01 natural sciences Connection (mathematics) 010104 statistics & probability Linear regression Statistics::Methodology Applied mathematics Equivariant map 0101 mathematics Statistics Probability and Uncertainty Mathematics |
| Zdroj: | Statistical Methods & Applications. 29:79-99 |
| ISSN: | 1613-981X 1618-2510 |
| Popis: | A seemingly unrelated regression model has been commonly used for describing a set of different regression models with correlations. This paper discusses the estimation of the covariance matrix in a seemingly unrelated regression model under Stein’s loss function. In particular, when the correlation matrix is assumed to be known, a best equivariant estimator of the covariance matrix is derived. Its properties are investigated and a connection to a best equivariant estimator of regression coefficients given in a previous study is shown. Results of numerical simulations and an illustrative example are also presented to compare the best equivariant estimator of the covariance matrix with several conventional covariance matrix estimators. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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