Smoothed Regression Estimates with Pearson Noise

Abstract

Li and Hwang (1984) examined estimates for a regression problem which are a compromise between the naive, raw data estimate and a nonparametric estimate. They developed such compromise, or smoothed, estimates assuming Gaussian noise. This report, develops method regression estimates in the more general case in which Pearson noise is present. Estimates are established having good Bayes risk and ordinary risk. Keywords: Bayes estimate, Dominant estimate, Pearson variate, Regression, Simultaneous estimation, Squared error loss.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1988
Accession Number
ADA198973

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  • Wayne Johnson

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  • Statistical inference.