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Smoothed categorical regression based on direct kernel estimates
Abstract:A nonparametric method is considered which yields smoothed estimates of the response probabilities when the response variable is categorical. The method is based on Lauder's (1983) direct kernel estimates which are extended to allow for ordinal kernels. Thus one can make use of the ordinal scale of the response variable. A class of predictive loss functions is introduced on which the cross-validatory choice of smoothing parameters is based. Plots of the smoothed response probabilities may be used to uncover the form of covariate effects
Keywords:Smoothed Regression  Categorical Data  Ordinal Data  Discrete Kernel  direct kernel  Cross-Validation
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