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《Chinese Journal of Applied Probability and Statistics》 2010-01
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A LASSO-Type Approach to Variable Selection and Estimation for Censored Regression Model

Wang Zhanfeng Wu Yaohua Zhao Lincheng(Department of Statistics and Finance,University of Science and Technology of China,Hefei,230026)  
Censored regression("Tobit") model is one of important regression models and has been widely used in econometrics.However,studies for variable selection problem in censored regression model are rare at the present references.In this paper,for censored regression model we propose a LASSO-type approach,diverse penalty L1 constraint method(DPLC),to select variables and estimate the corresponding coefficients.Furthermore,we obtain the asymptotic properties of nonzero elements' estimation of regression coefficient.Finally,extensive simulation studies show that DPLC method almost possesses the same performance of selecting variables and estimation as generally best subset selection method(GBSS).
【Fund】: supported by National Natural Science Foundation of China (10471136 and 10671189);; the Knowledge Innovation Program of the Chinese Academy of Sciences (KJCX3-SYW-S02)
【CateGory Index】: O212.1
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【Citations】
Chinese Journal Full-text Database 2 Hits
1 JIN MAN FANG YIXIN ZHAO LINCHENG (Department of Statistics and Finance, University of Science and Technology of China, Hefei, 230026);Variable Selection for Censored Regression Models[J];Chinese Journal of Applied Probability and Statisties;2005-02
2 ZHAO Liucheng FANG Yixin (Department of Statistics and Finance, University of Science and Technology of China, Hefei 230026, China);RANDOM WEIGHTING METHOD FOR CENSORED REGRESSION MODEL[J];系统科学与复杂性学报(英文版);2004-02
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