A fast algorithm for the nonparametric maximum likelihood estimate in the Cox-Gene model
學年 95
學期 2
出版(發表)日期 2007-07-01
作品名稱 A fast algorithm for the nonparametric maximum likelihood estimate in the Cox-Gene model
作品名稱(其他語言)
著者 Chang, I. S.; Wen, C. C.; Wu, Y. J.; Yang, C. C.
單位 淡江大學數學學系
出版者 Taipei: Academia Sinica * Institute of Statistical Science
著錄名稱、卷期、頁數 Statistica Sinica 17(3), p.841-855
摘要 The Cox model with the gene effect for age at onset was introduced and studied by Li, Thompson and Wijsman (1998) and Li and Thompson (1997). This paper concerns the numerical performance of the nonparametric maximum likelihood estimate of the environmental effects and the genetic effect in this model. Based on the self-consistency equations derived from the score functions, we propose a fast iterative algorithm for the computations of the nonparametric maximum likelihood estimate and its asymptotic variance. Simulation studies conducted using these algorithms indicate that the profile likelihood-based normal approximations for the estimates are valid with reasonable sample sizes, and the bootstrap methods work well also for smaller sample sizes, and are computationally feasible.
關鍵字
語言 en_US
ISSN 1017-0405
期刊性質 國內
收錄於 SCI
產學合作
通訊作者
審稿制度
國別 TWN
公開徵稿
出版型式 ,紙本
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