CSpace
confidencelowerlimitsforresponseprobabilitiesunderthelogisticresponsemodel
Tian Yubin1; Li Guoying2; Yang Jie1
2004
Source Publicationjournalofsystemsscienceandcomplexity
ISSN1009-6124
Volume017Issue:002Pages:289
AbstractThe lower confidence limits for response probabilities based on binary response data under the logistic response model are considered by saddlepoint approach.The high order approximation to the conditional distribution of a statistic for an interested parameter and then the lower confidence limits of response probabilities are derived.A simulation comparing these lower confidence limits with those obtained from the asymptotic normality is conducted.The proposed approximation is applied to two real data sets.Numerical results show that the saddlepoint approximations are much more accurate than the asymptotic normality approximations,especially for the cases of small or moderate sample sizes.
Language英语
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/40381
Collection中国科学院数学与系统科学研究院
Affiliation1.北京理工大学
2.中国科学院数学与系统科学研究院
Recommended Citation
GB/T 7714
Tian Yubin,Li Guoying,Yang Jie. confidencelowerlimitsforresponseprobabilitiesunderthelogisticresponsemodel[J]. journalofsystemsscienceandcomplexity,2004,017(002):289.
APA Tian Yubin,Li Guoying,&Yang Jie.(2004).confidencelowerlimitsforresponseprobabilitiesunderthelogisticresponsemodel.journalofsystemsscienceandcomplexity,017(002),289.
MLA Tian Yubin,et al."confidencelowerlimitsforresponseprobabilitiesunderthelogisticresponsemodel".journalofsystemsscienceandcomplexity 017.002(2004):289.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Tian Yubin]'s Articles
[Li Guoying]'s Articles
[Yang Jie]'s Articles
Baidu academic
Similar articles in Baidu academic
[Tian Yubin]'s Articles
[Li Guoying]'s Articles
[Yang Jie]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Tian Yubin]'s Articles
[Li Guoying]'s Articles
[Yang Jie]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.