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LAD estimation for nonlinear regression models with randomly censored data

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Abstract

The least absolute deviations (LAD) estimation for nonlinear regression models with randomly censored data is studied and the asymptotic properties of LAD estimators such as consistency, boundedness in probability and asymptotic normality are established. Simulation results show that for the problems with censored data, LAD estimation performs much more robustly than the least squares estimation.

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Authors and Affiliations

  1. School of Mathematics and Computer Science, Nanjing Normal University, 210097, Nanjing, China

    Zhou Xiuqing

  2. Department of Mathematics, Nanjing University, 210093, Nanjing, China

    Zhou Xiuqing & Wang Jinde

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  1. Zhou Xiuqing
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  2. Wang Jinde
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Correspondence to Zhou Xiuqing.

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Xiuqing, Z., Jinde, W. LAD estimation for nonlinear regression models with randomly censored data. Sci. China Ser. A-Math. 48, 880–897 (2005). https://doi.org/10.1007/BF02879071

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  • DOI: https://doi.org/10.1007/BF02879071

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