
Accession Number : ADA186025
Title : Strong Consistency of Estimation of Number of Regression Variables when the Errors are Independent and Their Expectations are not Equal to Each Other.
Descriptive Note : Technical rept.,
Corporate Author : PITTSBURGH UNIV PA CENTER FOR MULTIVARIATE ANALYSIS
Personal Author(s) : Wu, Yuehua
PDF Url : ADA186025
Report Date : Jun 1987
Pagination or Media Count : 27
Abstract : This document considers the linear regression model y sub i = x sub i B + e sub i, i = 1, 2, ..., where (x sub i)  is a sequence of known pvectors, Beta = (Beta Sub 1, ..., Beta Sub p) is an unknown pvector, known as regression coefficients, (e Sub i) is a sequence of random errors. It is of interest to test the hypothesis H Sub k: Beta Sub k+1 = ... = Beta Sub p = O, k = O, 1,...,p. We do not assume that the random errors are identically distributed and have zero means, since it is sometimes realistic. As a compensation for this relaxation, we assume the errors have a common bounded support A Sub 1, a Sub 2 under certain conditions, we obtain the strongly consistent estimate of the number k for which Beta Sub k is not equal to O and Beta Sub k+1 = ... = Beta Sub p = O, by using the information theoretical criteria.
Descriptors : *VARIABLES, COEFFICIENTS, CONSISTENCY, ERRORS, HYPOTHESES, STATISTICAL INFERENCE, MULTIVARIATE ANALYSIS, MATHEMATICAL MODELS, REGRESSION ANALYSIS, SEQUENCES
Subject Categories : Statistics and Probability
Distribution Statement : APPROVED FOR PUBLIC RELEASE