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Asymptotic normality of the lengths of a class of nonparametric confidence intervals for a regression parameter
Authors:Madan L. Puri  Tiee-Jian Wu
Affiliation:1. Department of Mathematics Swain Hall East Indiana University Bloomington, Indiana 47405 U.S.A.

Research supported by National Science Foundation Grant MCS 8301409.;2. Department of Mathematics University of Houston University Park Houston, Texas 77004 U.S.A.

Abstract:
In the linear regression model, the asymptotic distributions of certain functions of confidence bounds of a class of confidence intervals for the regression parameter arc investigated. The class of confidence intervals we consider in this paper are based on the usual linear rank statistics (signed as well as unsigned). Under suitable assumptions, if the confidence intervals are based on the signed linear rank statistics, it is established that the lengths, properly normalized, of the confidence intervals converge in law to the standard normal distributions; if the confidence intervals arc based on the unsigned linear rank statistics, it is then proved that a linear function of the confidence bounds converges in law to a normal distribution.
Keywords:Rank statistics  linear regression model  confidence intervals  asymptotic normality
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