Classification via kernel regression based on univariate product density estimators
نویسندگان
چکیده
We propose a nonparametric discrimination method based on a nonparametric Nadaray-Watson kernel regression type-estimator of the posterior probability that an incoming observed vector is a given class. To overcome the curse of dimensionality of the multivariate kernel density estimate, we introduce a variance stabilizing approach which constructs independent predictor variables. Then, the multivariate kernel estimator is replaced by the univariate kernel product estimators. The new procedure is illustrated in simulated data sets and real example, confirming the usefulness of our approach.
منابع مشابه
Classification via kernel product estimators
Multivariate kernel density estimation is often used as the basis for a nonparametric classification technique. However, the multivariate kernel classifier suffers from the curse of dimensionality, requiring inordinately large sample sizes to achieve a reasonable degree of accuracy in high dimensional settings. A variance stabilising approach to kernel classification can be motivated through an...
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