Penalized Spline Approaches for Functional Principal Component Logit Regression

被引：0

作者：

Aguilera, A. ^{[1
,2
]}

Aguilera-Morillo, M. C. ^{[1
,2
]}

Escabias, M. ^{[1
,2
]}

Valderrama, M. ^{[1
,2
]}

机构：

[1] Univ Granada, Dept Stat, Granada, Spain

[2] Univ Granada, OR, Granada, Spain

来源：

RECENT ADVANCES IN FUNCTIONAL DATA ANALYSIS AND RELATED TOPICS | 2011年

关键词：

CURVES;

D O I：

暂无

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

The problem of multicollinearity associated with the estimation of a functional logit model can be solved by using as predictor variables a set of functional principal components. The functional parameter estimated by functional principal component logit regression is often unsmooth. To solve this problem we propose two penalized estimations of the functional logit model based on smoothing functional PCA using P-splines.

引用

页码：1 / 7

页数：7