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ROBUST TEST BASED ON NONLINEAR REGRESSION QUANTILE ESTIMATORS
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 Title & Authors
ROBUST TEST BASED ON NONLINEAR REGRESSION QUANTILE ESTIMATORS
CHOI, SEUNG-HOE; KIM, KYUNG-JOONG; LEE, MYUNG-SOOK;
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 Abstract
In this paper we consider the problem of testing statistical hypotheses for unknown parameters in nonlinear regression models and propose three asymptotically equivalent tests based on regression quantiles estimators, which are Wald test, Lagrange Multiplier test and Likelihood Ratio test. We also derive the asymptotic distributions of the three test statistics both under the null hypotheses and under a sequence of local alternatives and verify that the asymptotic relative efficiency of the proposed test statistics with classical test based on least squares depends on the error distributions of the regression models. We give some examples to illustrate that the test based on the regression quantiles estimators performs better than the test based on the least squares estimators of the least absolute deviation estimators when the disturbance has asymmetric and heavy-tailed distribution.
 Keywords
nonlinear regression quantiles estimators;Wald test;Lagrange multiplier test;likelihood ratio test;
 Language
English
 Cited by
1.
Estimation in a change-point nonlinear quantile model, Communications in Statistics - Theory and Methods, 2016, 0  crossref(new windwow)
2.
Generalized Analysis-of-variance-type Test for the Single-index Quantile Model, Communications in Statistics - Theory and Methods, 2015, 44, 13, 2842  crossref(new windwow)
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