An application of local likelihood methods in classification
Date Issued
2011
Date
2011
Author(s)
Lin, Hsiao-Lin
Abstract
In nonparametric discriminant analysis, we use local logistic regression model to estimate the posterior probability of Bayes rule. Before we carry on local logistic regression, we need to choose the smoothing parameter. We choose the smoothing parameter by minimizing the GCV. A simulation study suggests that this approach performs well.
Subjects
nonparametric
discriminant analysis
classification
Logistic regression
Bayes rule
Type
thesis
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ntu-100-R95221028-1.pdf
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