Author： P. A. Naik Chih-Ling Tsai
Publisher： Taylor & Francis Ltd
ISSN： 1048-5252
Source： Journal of Nonparametric Statistics, Vol.16, Iss.1-2, 2004-02, pp. : 187-195
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Abstract
We develop a residual information criterion (RIC) for single-index models using the residual log-likelihood approach. The proposed criterion selects both the smoothing parameter and explanatory variables. Thus, it is a general selection criterion that provides a unified approach to model selection across both parametric and nonparametric functions. Monte Carlo studies demonstrate that RIC performs satisfactorily except when the sample size is small and the signal-to-noise ratio is weak. An application of RIC is illustrated for marketing a new medical technology.
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