bootstrap;
importance resampling;
Markov chains;
maximum likelihood estimate;
probit model;
sequential design;
up and down method;
D O I:
暂无
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:
We start with a data set recently obtained from a Bruceton test. The data come from the study of CS-M-3 ignitor in a military experiment and are analyzed by the up-and-down method of Dixon and Mood (1948). Wie reexamine the method and develop a more appropriate inference that takes account of the special dependent data structure. Two bootstrap confidence interval procedures, percentile and bootstrap-t, are introduced to find approximate confidence intervals for the parameters of interest. A simulation study shows that the bootstrap-t, with proper bias corrections, gives better coverage probability, but is considerably more computer-intensive than non-bias-corrected versions. This leads to the development of an importance resampling technique which can reduce the CPU time by a factor of 10 or more. Finally, we apply the proposed procedure to analyze our data set.
机构:
Univ Dayton, Dept Hlth & Sport Sci, Dayton, OH 45469 USAUniv Dayton, Dept Hlth & Sport Sci, Dayton, OH 45469 USA
Beerse, Matthew
Lelko, Michael
论文数: 0引用数: 0
h-index: 0
机构:
Georgia State Univ, Dept Kinesiol & Hlth, 125 Decatur St, Atlanta, GA 30302 USAUniv Dayton, Dept Hlth & Sport Sci, Dayton, OH 45469 USA
Lelko, Michael
Wu, Jianhua
论文数: 0引用数: 0
h-index: 0
机构:
Georgia State Univ, Dept Kinesiol & Hlth, 125 Decatur St, Atlanta, GA 30302 USA
Georgia State Univ, Ctr Movement & Rehabil Res, Atlanta, GA 30302 USAUniv Dayton, Dept Hlth & Sport Sci, Dayton, OH 45469 USA