Model based bootstrap methods for interval censored data

被引:23
作者
Sen, Bodhisattva [1 ]
Xu, Gongjun [2 ]
机构
[1] Columbia Univ, Dept Stat, New York, NY 10027 USA
[2] Univ Minnesota, Sch Stat, Minneapolis, MN 55455 USA
基金
美国国家科学基金会;
关键词
Consistency of bootstrap; Current status data; Mixed-case interval censoring; Nonparametric maximum likelihood estimator; ESTIMATOR; REGRESSION; ALGORITHM; SELECTION;
D O I
10.1016/j.csda.2014.07.007
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The performance of model based bootstrap methods for constructing point-wise confidence intervals around the survival function with interval censored data is investigated. It is shown that bootstrapping from the nonparametric maximum likelihood estimator of the survival function is inconsistent for the current status model. A model based smoothed bootstrap procedure is proposed and proved to be consistent. In fact, a general framework for proving the consistency of any model based bootstrap scheme in the current status model is established. In addition, simulation studies are conducted to illustrate the (in)-consistency of different bootstrap methods in mixed case interval censoring. The conclusions in the interval censoring model would extend more generally to estimators in regression models that exhibit non-standard rates of convergence. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码:121 / 129
页数:9
相关论文
共 28 条
[1]   On the bootstrap of the maximum score estimator [J].
Abrevaya, J ;
Haung, J .
ECONOMETRICA, 2005, 73 (04) :1175-1204
[2]  
[Anonymous], 2012, The Jackknife and Bootstrap
[3]  
[Anonymous], 1993, An introduction to the bootstrap
[4]  
Banerjee M, 2001, ANN STAT, V29, P1699
[5]   SOME ASYMPTOTIC THEORY FOR THE BOOTSTRAP [J].
BICKEL, PJ ;
FREEDMAN, DA .
ANNALS OF STATISTICS, 1981, 9 (06) :1196-1217
[6]  
Brunk HD, 1970, NONPARAMETRIC TECHNI, P195
[7]   A SEMIPARAMETRIC MODEL FOR REGRESSION-ANALYSIS OF INTERVAL-CENSORED FAILURE TIME DATA [J].
FINKELSTEIN, DM ;
WOLFE, RA .
BIOMETRICS, 1985, 41 (04) :933-945
[8]   Bootstrap selection of the smoothing parameter in nonparametric hazard bate estimation [J].
GonzalezManteiga, W ;
Cao, R ;
Marron, JS .
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 1996, 91 (435) :1130-1140
[9]   Computing Chernoff's distribution [J].
Groeneboom, P ;
Wellner, JA .
JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 2001, 10 (02) :388-400
[10]  
Groeneboom P, 1992, DMV SEM, V19