Paper Info
Title
A High-dimensionality-adjusted Consistent Cp-type Statistic for Selecting Variables in a Normality-assumed Linear Regression with Multiple Responses.
Abstract
In this paper, we consider the consistency of Cp-type statistics for selecting variables in a normality-assumed linear regression with multiple responses when the dimension of the vector of the response variables may be large. We propose a new consistent Cp-type statistic for which consistency can be achieved whenever the dimension of the response variables vector is fixed or goes to infinity. A high probability of selecting the true subset of explanatory variables can be expected under a moderate sample size when the proposed Cp-type statistic is used to select variables, even when there is a high-dimensional response variables vector.
Year
DOI
Venue
2016
10.1016/j.procs.2016.08.151
KES
Keywords
Field
DocType
consistency,generalized C-p statistic,moderately high-dimensional data,multivariate linear regression model,multivariate normal distribution,variable selection
Computer science,Regression analysis,PRESS statistic,Proper linear model,Design matrix,Statistics,Linear predictor function,Marginal distribution,Linear regression,Ancillary statistic
Conference
Volume
Issue
ISSN
96
C
1877-0509
Citations 
PageRank 
References 
0
0.34
0
Authors
1
Name
Order
Citations
PageRank
Hirokazu Yanagihara1218.66