Powell (J. Econometrics 25 (1984) 303) considered censored regression model, and established the asymptotic normality of the least absolute deviation (LAD) estimator. But the asymptotic covariance matrices depend on the error density and are therefore difficult to estimate reliably. In the earlier papers, this difficulty may be solved by applying the bootstrap method (see, e.g., Hahn (J. Econometric Theory 11 (1995) 105); Bilias et al. (J. Econometrics 99 (2000) 373). In this paper we propose a random weighting method to approximate the distribution of the LAD estimator. The random weighting method was developed by Rubin (Ann. Statist. 9 (1981) 130), Lo (Ann. Statist. 15 (1987) 360), Tu and Zheng (Chinese J. Appl. Probab. Statist. 3 (1987) 340) with reference to some statistics such as the sample mean. Rao and Zhao (Sankhya 54 (1992) 323) applied random weighting method to approximate asymptotic distribution of M-estimators in regression models. In this paper we extend this method to the censored regression model.
All Science Journal Classification (ASJC) codes
- Applied Mathematics
- Statistics and Probability
- Statistics, Probability and Uncertainty