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Asymptotic normality of the lengths of a class of nonparametric confidence intervals for a regression parameter
Author(s) -
Puri Madan L.,
Wu TieeJian
Publication year - 1984
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.2307/3314751
Subject(s) - mathematics , cdf based nonparametric confidence interval , statistics , confidence interval , confidence distribution , robust confidence intervals , confidence region , linear regression , nonparametric statistics , asymptotic distribution , rank (graph theory) , combinatorics , estimator
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.