SOME VARIABLE SELECTION PROCEDURES IN MULTIVARIATE LINEAR-REGRESSION MODELS

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/27821

Title:	SOME VARIABLE SELECTION PROCEDURES IN MULTIVARIATE LINEAR-REGRESSION MODELS
Authors:	HUANG,DY;LIU,KC
Contributors:	統計研究所
Keywords:	CP
Date:	1994
Issue Date:	2010-06-29 19:34:21 (UTC+8)
Publisher:	中央大學
Abstract:	In this paper, we propose a two-stage variable selection procedure for multivariate linear regression models. We select appropriate models under a guaranteed probability by using the summation of noncentralities in the first stage. In the second stage, we exclude those models with large individual noncentrality, and then select the best model with the minimum Akaike's information criterion (AIC). Empirical study is provided to show how to achieve our goal in variable selection and to demonstrate the efficiency and usefulness of the procedure in practical applications. In addition, we have built a reasonable model to ''plain and predict the earnings and productivity in Taiwan area.
Relation:	JOURNAL OF STATISTICAL PLANNING AND INFERENCE
Appears in Collections:	[Graduate Institute of Statistics] journal & Dissertation

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