The strong consistency of the $L_1$-norm estimators in censored nonlinear regression models

  • Park, Seung-Hoe (Department of Gemeral Studies, Hankuk Ayiation University, Koyang 411-791) ;
  • Kim, Hae-Kyung (Department of Mathematics, Yonsei University)
  • Published : 1997.11.01

Abstract

This paper is concerned with the strong consistency of the $L_1$-norm estimators for the nonlinear regression models when dependent variables are subject to censoring, and provides the sufficient conditions which ensure the strong consistency of $L_1$-norm estimators of the censored regression models.

References

  1. Econometrica v.41 Regression Analysis when the Dependent Variable is Truncated Normal Amemiya, T.
  2. Communication in Statistics v.23 Consistency of $L_1$ Estimates in Censored Linear Regression Models Chen, X.R.;Wu, Y.
  3. Bulletin of the Korean Mathematical Society v.33 The Consistency Estimation in Nonlinear Regression Models with Noncompact Parameter Space Choi, S.H.;Kim, H.K.;Jang, S.H.
  4. Journal of Korean Statististics Society v.24 Asymtotic Properties of Nonlinear Least Absolute Deviation Estimators Kim, H.K.;Choi, S.H.
  5. Econometrica v.5 Uniform Convergence in Probability and Stochastic Equicontinuity Newey, W.K.
  6. Journal of Econometrics v.25 Least Absoulte Deviation Estimation for the Censored Regression Model Powell, J.L.
  7. Journal of Econometrics v.27 Censored Regression Quantiles Powell, J.L.