NDLI: Approximation by randomly weighting method in censored regression model

Content Provider	Springer Nature Link
Author	Wang, ZhanFeng Wu, YaoHua Zhao, LinCheng
Copyright Year	2009
Abstract	Censored regression (“Tobit”) models have been in common use, and their linear hypothesis testings have been widely studied. However, the critical values of these tests are usually related to quantities of an unknown error distribution and estimators of nuisance parameters. In this paper, we propose a randomly weighting test statistic and take its conditional distribution as an approximation to null distribution of the test statistic. It is shown that, under both the null and local alternative hypotheses, conditionally asymptotic distribution of the randomly weighting test statistic is the same as the null distribution of the test statistic. Therefore, the critical values of the test statistic can be obtained by randomly weighting method without estimating the nuisance parameters. At the same time, we also achieve the weak consistency and asymptotic normality of the randomly weighting least absolute deviation estimate in censored regression model. Simulation studies illustrate that the performance of our proposed resampling test method is better than that of central chi-square distribution under the null hypothesis.
Starting Page	561
Ending Page	576
Page Count	16
File Format	PDF
ISSN	10069283
Journal	Science in China Series A: Mathematics
Volume Number	52
Issue Number	3
e-ISSN	18622763
Language	English
Publisher	SP Science in China Press
Publisher Date	2009-03-15
Publisher Place	Heidelberg
Access Restriction	Subscribed
Subject Keyword	censored regression model least absolute deviation asymptotic normality local alternative randomly weighting method asymptotic power Hypothesis testing Asymptotic properties Estimation Applications of Mathematics
Content Type	Text
Resource Type	Article
Subject	Mathematics

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