Multi-class logistic discrimination via wavelet-based functionalization and model selection criteria
説明
We consider a multi-class logistic discrimination for functional data. We use a wavelet-based smoothing technique in obtaining a set of functional data, from irregularly sampled time-dependent covariates of a number of individuals. A method of estimating discriminant model is based on a regularized log-likelihood, where we apply model selection criteria derived from Kullback-Leibler information and Bayes’ analysis.
収録刊行物
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- MHF Preprint Series
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MHF Preprint Series 2006-25 2006-07-27
Faculty of Mathematics, Kyushu University