A monte carlo study was conducted to examine the
performance of several strategies for estimating the
squared cross-validity coefficient of a sample regression
equation in the context of best subset regression.
Data were simulated for populations and experimental
designs likely to be encountered in practice. The results
indicated that a formula presented by Stein
(1960) could be expected to yield estimates as good as
or better than cross-validation, or several other formula
estimators, for the populations considered. Further,
the results suggest that sample size may play a
much greater role in validity estimation in subset selection
than is true in situations where selection has
not occurred. Index terms: Best subset regression,
Cross-validity coefficient, Multiple regression, Predictive
validity, Variable selection.
Kennedy, Eugene. (1988). Estimation of the squared cross-validity coefficient in the context of best subset regression. Applied Psychological Measurement, 12, 231-237. doi:10.1177/014662168801200302
Estimation of the squared cross-validity coefficient in the context of best subset regression.
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