<プレプリント>
Regularized Functional Regression Modeling for Functional Response and Predictors

作成者
本文言語
出版者
発行日
収録物名
出版タイプ
アクセス権
関連情報
関連URI
概要 We consider the problem of constructing a functional regression modeling procedure with functional predictors and a functional response. Discretely observed data set are expressed by Gaussian basis ex...pansions for individuals, using smoothing methods. Parameters involved in the functional regression model are estimated by the regularized maximum likelihood method, assuming that coefficient functions are expressed by basis expansions. For the selection of regularization parameters involved in the regularization method, we extend information theoretic and Bayesian model selection criteria for evaluating the estimated model. The proposed modeling strategy is applied to the analysis of real data, predicting functions rather than scalars.続きを見る

本文ファイル

MI2009-2 pdf 405 KB 64  

詳細

レコードID
査読有無
権利関係
主題
注記
タイプ
登録日 2009.09.02
更新日 2018.01.25

この資料を見た人はこんな資料も見ています