On Eliminating Inferior Regression Models.
PURDUE UNIV LAFAYETTE IN DEPT OF STATISTICS
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Consider a linear regression model with p-1 predictor variables which is taken as the true model. The goal is to select of all possible reduced models such that all inferior models to be defined are excluded with a guaranteed minimum probability. A procedure is proposed for which the exact evaluation of the probability of a correct decision is difficult however, it is shown that the probability requirement can be met for sufficiently large sample size. Monte Carlo evaluation of the constant associated with the procedure and some ways to reduce the amount of computations involved in the implementation of the procedure are discussed. Author
- Statistics and Probability