Semiparametric regression model selections

In semiparametric regression models, we have developed a small-sample criterion, AICC, for the selection of explanatory variables in the parametric component as well as for choosing the number of spline knots to estimate the nonparametric component. Tn contrast to the Akaike Information Criterion (AIC), AICC provides a nearly unbiased estimator of the expectation of the Kullback-Leibler information. Monte Carlo results show that AICC outperforms AIC, CP (Mallows, 1973), FPE (Akaike, 1970), and SIC (Schwartz, 1978) for small samples. In addition, we show that AICC, AIC, Cp, FPE, and GCV provide asymptotically efficient selections. The asymptotic optimalities of GIC (Nishii, 1984) and SIC are also obtained. (C) 1999 Elsevier Science B.V. All rights reserved.Statistics & ProbabilitySCI(E)0ARTICLE1119-1397