Paper Info
Title
Variable selection by Cp statistic in multiple responses regression with fewer sample size than the dimension
Abstract
In this paper, we introduce a better statistical method about model selection, and contribute to updating data mining technique. We consider the problem of selecting q explanatory variables out of k(q ≤ k), when the dimension p of the response variables is larger than the sample size n in the multiple responses regression. We consider Cp statistic which is an estimator of the sum of standardized mean square errors. The standardization uses the inverse of the variance-covariance matrix of p response variables and thus the estimator of the inverse of the sample variance-covariance matrix. However, since n p, such an inverse matrix cannot be used. Thus, we use the Moore-Penrose inverse and define the Cp statistic. Such a statistic will be denoted by Cp+. An example is given to illustrate the use of Cp+ statistic. The performance is demonstrated by simulation result and real data study.
Year
Venue
Keywords
2010
KES (3)
Moore-Penrose inverse,variance-covariance matrix,sample variance-covariance matrix,Cp statistic,inverse matrix,q explanatory variable,p response variable,data mining technique,multiple responses regression,fewer sample size,dimension p,n p,variable selection
DocType
Volume
ISSN
Conference
6278
0302-9743
ISBN
Citations 
PageRank 
3-642-15392-5
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Mariko Yamamura100.68
Hirokazu Yanagihara2218.66
Muni S. Srivastava37617.08