数学物理学报(英文版) ›› 1996, Vol. 16 ›› Issue (2): 192-208.

• 论文 • 上一篇    下一篇

ESTIMATION FOR THE AYMPTOTIC VARIANCE OF PARAMETRIC ESTIMATES IN PARTIAL LINEAR MODEL WITH CENSORED DATA

秦更生, 蔡雷   

  1. Dept. of Math., Sichuan University, Chengdu 610064, China
  • 收稿日期:1994-01-22 修回日期:1995-01-15 出版日期:1996-06-25 发布日期:1996-06-25
  • 基金资助:
    The project supported by national natural science foundation of China.

ESTIMATION FOR THE AYMPTOTIC VARIANCE OF PARAMETRIC ESTIMATES IN PARTIAL LINEAR MODEL WITH CENSORED DATA

Qin Gengsheng, Cai Lei   

  1. Dept. of Math., Sichuan University, Chengdu 610064, China
  • Received:1994-01-22 Revised:1995-01-15 Online:1996-06-25 Published:1996-06-25
  • Supported by:
    The project supported by national natural science foundation of China.

摘要: Consider tile partial linear model Y=+ g(T) + e. Where Y is at risk of being censored from the right, g is an unknown smoothing function on[0,1], β is a 1-dimensional parameter to be estimated and e is an unobserved error. In Ref[1,2], it wes proved that the estimator Σn* ≡(θn*)-2 En*n*≡(θn*)-2Ên*) for the asymptotic variance of βn*(βn*) is consistent. In this paper, we establish the limit distribution and the law of the iterated logarithm for En*, and obtain the convergest rates for En* and the strong uniform convergent rates for ĝn*(gn*).

关键词: Partial linear model, Censored data, Kernel method, Asymptotic normality, The law of the iterated logarithm

Abstract: Consider tile partial linear model Y=+ g(T) + e. Where Y is at risk of being censored from the right, g is an unknown smoothing function on[0,1], β is a 1-dimensional parameter to be estimated and e is an unobserved error. In Ref[1,2], it wes proved that the estimator Σn* ≡(θn*)-2 En*n*≡(θn*)-2Ên*) for the asymptotic variance of βn*(βn*) is consistent. In this paper, we establish the limit distribution and the law of the iterated logarithm for En*, and obtain the convergest rates for En* and the strong uniform convergent rates for ĝn*(gn*).

Key words: Partial linear model, Censored data, Kernel method, Asymptotic normality, The law of the iterated logarithm