Approximation by randomly weighting method in censored regression model |
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Authors: | ZhanFeng Wang YaoHua Wu LinCheng Zhao |
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Institution: | (1) Department of Statistics and Finance, University of Science and Technology of China, Hefei, 230026, China |
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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.
This work was supported by National Natural Science Foundation of China (Grant No. 10471136), PhD Program Foundation of the
Ministry of Education of China, and Special Foundations of the Chinese Academy of Sciences and University of Science and Technology
of China |
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Keywords: | censored regression model least absolute deviation asymptotic normality local alternative randomly weighting method asymptotic power |
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