A distribution-free least squares estimator for censored linear regression models |
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| Institution: | 1. Department of Chemical Engineering and Applied Chemistry, Chungnam National University, 220 Gung-Dong, Yuseong-Gu, Daejeon, 305-764, Republic of Korea;2. Department of Materials Science and Engineering, Chungnam National University, 220 Gung-Dong, Yuseong-Gu, Daejeon, 305-764, Republic of Korea;3. Department of Chemistry, Chungnam National University, 220 Gung-dong, Yuseong-gu, Daejeon, 305-764, Republic of Korea;4. R&D Planning Team, GS Caltex Institute of Technology, 359, Expo-ro, Yuseong-gu, Daejeon, 34122, Republic of Korea;1. School of Civil Engineering, Purdue University, 47907 West Lafayette, IN, United States;2. Department of Information Engineering and Computer Science, University of Trento, 38123 Trento, Italy;1. Natural Resources Canada, Canadian Forest Service, 506 West Burnside Road, Victoria, BC V8Z 1M5, Canada;2. Chair of Land Use Engineering, ETH Zurich, CH 8092 Zurich, Switzerland;3. Swiss Federal Research Institute, WSL, Zürcherstrasse 111, 8903 Birmensdorf ZH, Switzerland;4. Norwegian University of Life Sciences, P.O. Box 5003, NO1432 Ås, Norway |
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| Abstract: | This paper describes a method for estimating simultaneously the parameter vector of the systematic component and the distribution function of the random component of a censored linear regression model. The estimator is obtained by minimizing the sum of the squares of the differences between the observed values of the dependent variable and the corresponding expected values of this variable according to the estimated parameter vector and distribution function. The resulting least squares parameter estimator incorporates information on the distribution of the random component of the regression model that is available from the estimation sample. Hence, it may often be more efficient than are parameter estimators that do not use such information. The results of numerical experiments with the least squares estimator tend to support this hypothesis. |
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