收藏本站
收藏 | 手机打开
二维码
手机客户端打开本文

Estimation and Modelling of Event Time Data

Alii Leonard Kitl  
【摘要】:With availability of each individual event times, inference for point processes is most efficient. However there are times when the study de-sign is such that only count data are collected, consisting of the number of events or recurrences for each individual over the entire follow-up times. This thesis discusses the loss in efficiency of an analysis of such count data versus an analysis of the actual-event times. One particu-lar case is exemplified, that in which the purpose of the experiment or trial is to compare the effects of treatments, and the loss in efficiency in the estimator of the treatment effect is computed. The specific point process considered here is the non-homogeneous poison process, with a proportional intensity model for the treatment effects. Random effects models are also considered with estimation via a quasi-likelihood approach. The quasi likelihood analysis proposed here is an extension of such techniques for the homogeneous poison process. The resulting estimating equations for the parameters in the random effects models are simple and intuitive. The results show that for many usual situations treatment effects are efficiently estimated using aggregate data; however when only end of follow-up counts are collected, the underlying intensity function is not. Multiple follow-up count data is shown to recover much of the information lost by end of follow-up counts. The efficiency of the quasi likelihood estimators is shown to be high relative to specific likelihood alternatives. Tests and diagnostic procedures for checking model assumptions are presented.The quasi likelihood estimators developed here require the assumption of a para-metric form for the intensity function. This thesis also develops a non-parametric approach to the estimation of the intensity function. Combined with quasi likelihood estimators for covariates, this provides a simple method for the analysis of recurrent event data, requiring less stringent assumptions than traditional methods. We examine the small sample behavior of these procedures with simulation studies. The stud-ies show that for the situations we consider, the methods work well and display adequate small sample characteristics. Analyses of illustrative examples demonstrate the application of the procedures.


知网文化
【相似文献】
中国期刊全文数据库 前20条
1 ;Empirical likelihood estimation of discretely sampled processes of OU type[J];Science in China(Series A:Mathematics);2009年05期
2 ;Adaptive quasi-likelihood estimate in generalized linear models[J];Science in China,Ser.A;2005年06期
3 ;Variable selection using penalized empirical likelihood[J];Science China(Mathematics);2011年09期
4 周磊;周世东;姚彦;;Joint Maximum-Likelihood and MMSE Channel Estimator for UWB Communications[J];Tsinghua Science and Technology;2006年05期
5 ;ASYMPTOTIC NORMALITY OF MAXIMUM QUASI-LIKELIHOOD ESTIMATORS IN GENERALIZED LINEAR MODELS WITH FIXED DESIGN[J];Journal of Systems Science and Complexity;2008年03期
6 YANG YiPing;LI GaoRong;TONG TieJun;;Corrected empirical likelihood for a class of generalized linear measurement error models[J];Science China(Mathematics);2015年07期
7 ;Parametric estimation of discretely sampled Gamma-OU processes[J];Science in China(Series A:Mathematics);2006年09期
8 ;Consistency and Asymptotic Normality of the Maximum Quasi-likelihood Estimator in Quasi-likelihood Nonlinear Models with Random Regressors[J];Acta Mathematicae Applicatae Sinica(English Series);2010年02期
9 ;Quasi-likelihood estimation of average treatment effects based on model information[J];Science in China(Series A:Mathematics);2007年01期
10 Shan-shan WANG;Heng-jian CUI;;Partial Penalized Empirical Likelihood Ratio Test Under Sparse Case[J];Acta Mathematicae Applicatae Sinica;2017年02期
11 Hua ZHANG;Lu-ping XU;Yang-he SHEN;Rong JIAO;Jing-rong SUN;;A new maximum-likelihood phase estimation method for X-ray pulsar signals[J];Journal of Zhejiang University-Science C(Computers & Electronics);2014年06期
12 ;Maximum Likelihood Estimator for the Proportional Hazards Model with Incomplete Information[J];Wuhan University Journal of Natural Sciences;2012年02期
13 ;Convergence Rate of the L-N Estimator in Poisson-Gamma Models[J];Acta Mathematicae Applicatae Sinica(English Series);2006年04期
14 ;Empirical likelihood-based evaluations of Value at Risk models[J];Science in China(Series A:Mathematics);2009年09期
15 陈燕红;宋立新;;MA(q)模型的经验似然推断(英文)[J];数学研究与评论;2009年05期
16 ;Rate of strong consistency of the maximum quasi-likelihood estimator in quasi-likelihood nonlinear models[J];Applied Mathematics:A Journal of Chinese Universities(Series B);2008年04期
17 Hrdle Wolfgang;Empirical likelihood-based dimension reduction inference for linear error-in-responses models with validation study[J];Science in China,Ser.A;2004年06期
18 ;Empirical likelihood-based inferences for the area under the ROC curve with covariates[J];Science China(Mathematics);2012年08期
19 YUAN XiaoHui;LIN Nan;DONG XiaoGang;LIU TianQing;;Weighted quantile regression for longitudinal data using empirical likelihood[J];Science China(Mathematics);2017年01期
20 崔小准,胡光锐,陈豪;Maximum Likelihood Estimation of Clock Synchronization Error in OFDM System[J];Journal of DongHua University;2004年04期
中国重要会议论文全文数据库 前10条
1 ;Fuzzy Maximum Likelihood Estimator[A];第三届中国智能计算大会论文集[C];2009年
2 Huiping ZHUANG;Jieying Lu;Junhui LI;;Joint Estimation of State and Parameter with Maximum Likelihood Method[A];第36届中国控制会议论文集(D)[C];2017年
3 Chaoyi Shi;Qi Zhang;Tianguang Chu;;Source Identification of Network Diffusion Processes with Partial Observations[A];第36届中国控制会议论文集(G)[C];2017年
4 ;An Improved Approach for Estimating Empirical likelihood Based on Random Walk Metropolis Algorithm[A];2011年中国卫生统计学年会会议论文集[C];2011年
5 Chen Xia;;Quasi-likelihood Bridge Estimators for High-dimensional Generalized Linear Models[A];第十届海峡两岸统计与概率研讨会摘要集[C];2016年
6 王婉倫;;Approximate maximum likelihood approaches for multivariate nonlinear mixed-effects models[A];第十届海峡两岸统计与概率研讨会摘要集[C];2016年
7 ;Should FEV1/FEV6 Replace FEV1/FVC Ratios to Detect Airway Obstruction?[A];浙江省中西医结合呼吸病诊治进展暨第六次学术年会论文汇编[C];2008年
8 Hongli Chen;Qiang Li;Ziyuan Wang;;Improved maximum likelihood method for ship parameter identification[A];第37届中国控制会议论文集(A)[C];2018年
9 ;Maximum likelihood forgetting stochastic gradient estimation algorithm for Hammerstein CARARMA systems[A];第24届中国控制与决策会议论文集[C];2012年
10 Da-Peng Li;Ying-Qin Sun;Di Yao;;Maximum Likelihood Estimation of K-distribution Parameters Using Number Theoretic Methods[A];proceedings of 2010 3rd International Conference on Computer and Electrical Engineering (ICCEE 2010 no.2)[C];2012年
中国博士学位论文全文数据库 前4条
1 Alii Leonard Kitl;[D];华中师范大学;2012年
2 Muhammad Hanif;[D];浙江大学;2011年
3 吴远山;辅助数据问题和多元失效时间的半参数分析[D];武汉大学;2010年
4 王凯平;对独立及相依数据的非参和半参模型的半参调整[D];山东大学;2008年
中国硕士学位论文全文数据库 前4条
1 Ali Munsir;[D];南京理工大学;2018年
2 邹海(Ilyas Zouheir);[D];南京理工大学;2018年
3 胡金扣;鲁棒支持向量机研究[D];河北大学;2015年
4 Chonlanet Preechacharoensri;[D];北京林业大学;2013年
中国知网广告投放
 快捷付款方式  订购知网充值卡  订购热线  帮助中心
  • 400-819-9993
  • 010-62982499
  • 010-62783978