LONG-RUN PROPORTIONAL HAZARDS MODELS OF RANDOM CENSORSHIP

被引:5
作者
BEIRLANT, J [1 ]
CARBONEZ, A [1 ]
VANDERMEULEN, E [1 ]
机构
[1] CATHOLIC UNIV LEUVEN,DEPT MATH,B-3000 LOUVAIN,BELGIUM
关键词
RANDOM CENSORING; KOZIOL-GREEN MODEL; REGULAR VARIATION; WEAK CONVERGENCE; GAUSSIAN PROCESS;
D O I
10.1016/0378-3758(92)90150-Q
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
In the Koziol-Green or proportional hazards model of random censorship, the survival distribution (GBAR) or the censoring variable is a power function of the survival distribution FBAR of the lifetime, with the exponent in the power being the censoring parameter. In this paper, we propose a more general (semiparametric) model G(t)BAR = F(t)theta-BAR L(F(t))BAR, where L is some slowly varying function. A specific parametric example of an L-function is found to perform well for many well-known survival data sets. In this case estimators of the parameters and the survival function FBAR are proposed and asymptotics are given. This model is also compared with a recent proposal of Pena and Rohatgi (1989).
引用
收藏
页码:25 / 44
页数:20
相关论文
共 23 条
[1]  
ABDUSHUKUROV AA, 1987, NONPARAMETRIC ESTIMA
[2]  
ABDUSHUKUROV AA, 1984, C YOUNG SCI MATH I A
[4]   THE COMPARISON OF SURVIVAL CURVES [J].
ARMITAGE, P .
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-GENERAL, 1959, 122 (03) :279-300
[5]  
Bingham N.H., 1987, REGULAR VARIATION
[6]   LARGE SAMPLE STUDY OF LIFE TABLE AND PRODUCT LIMIT ESTIMATES UNDER RANDOM CENSORSHIP [J].
BRESLOW, N ;
CROWLEY, J .
ANNALS OF STATISTICS, 1974, 2 (03) :437-453
[7]   STRONG APPROXIMATIONS OF SOME BIOMETRIC ESTIMATES UNDER RANDOM CENSORSHIP [J].
BURKE, MD ;
CSORGO, S ;
HORVATH, L .
ZEITSCHRIFT FUR WAHRSCHEINLICHKEITSTHEORIE UND VERWANDTE GEBIETE, 1981, 56 (01) :87-112
[8]   MAXIMUM-LIKELIHOOD-ESTIMATION OF A SURVIVAL FUNCTION UNDER THE KOZIOL-GREEN PROPORTIONAL HAZARDS MODEL [J].
CHENG, PE ;
LIN, GD .
STATISTICS & PROBABILITY LETTERS, 1987, 5 (01) :75-80
[9]  
CHENG PE, 1984, B845 I STAT ACAD SIN
[10]  
Csorgo M., 1981, STRONG APPROXIMATION