Degree Type

Dissertation

Date of Award

1982

Degree Name

Doctor of Philosophy

Department

Statistics

Abstract

When using multiple regression models for predictive purposes, it may be desirable to exclude some possible regressor variables in order to reduce both the mean squared error (MSE) of the predictor and the cost incurred in taking observations. Some of the methods that have been proposed for this problem are discussed. The method of Lindley (1968) is detailed and extended to include the case of unknown variance (sigma)('2). A class of informative priors for the vector of unknown regression parameters is included;An evaluation of Lindley's predictor, with respect to bias and MSE, which recognizes that the predictor variables have been selected, is considered. For the case of a predictor based on two observed regressors, the numerical evaluations of the bias and the MSE of the predictor, conditional on selection, are performed for various configurations of data. The conditional bias and MSE of the predictor are numerically compared with the unconditional bias and MSE of the same predictor.

DOI

https://doi.org/10.31274/rtd-180813-7948

Publisher

Digital Repository @ Iowa State University, http://lib.dr.iastate.edu/

Copyright Owner

Peter David Christenson

Language

en

Proquest ID

AAI8307741

File Format

application/pdf

File Size

112 pages

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