Maximum penalized likelihood estimation for semi-parametric regression models with partly interval-censored failure time data
thesisposted on 2022-03-29, 00:10 authored by Jinqing Li
Interval-censored failure time data arise in many areas including demographical, financial, actuarial, medical and sociological studies. By interval censoring we mean that the failure time is not always exactly observed and we can only observe an interval within which the failure event has occurred. The goal of this dissertation is to develop maximum penalized likelihood (MPL) methods for ptoportional hazard (PH), additive hazard (AH) and accelerated failure time (AFT) models with partly interval-censored failure time data, which contains exactly observed, left-censored, finite interval-censored and right-censored data. We fit these three semi-parametric regression models by estimating the underlying non-parametric baseline hazard functions and regression coefficients. For the PH and AFT models, we compute these estimates simultaneously using the Newton and multiplicative iterative (Newton-MI) algorithm with line search steps, where the nonnegativity of baseline hazard functions is imposed in a direct way. For the AH model, we obtain the estimates using the primal-dual interior point algorithm, with which the baseline hazard function and hazard function are constrained to be non-negative simultaneously. The MPL methods provide smoothness for the baseline hazard estimates, which can clearly show the trend of how the baseline hazard estimates are changing over time. The asymptotic properties of these MPL estimators are studied. We investigate the performance of our proposed MPL methods by conducting simulation studies, and the simulation results demonstrate that our methods work well. In addition, we also make comparisons between our MPL methods and some existing methods. In a real data analysis, the proposed MPL methods are applied to the AIDS example provided by Lindsey and Ryan (1998).
Table of Contents1. Introduction -- 2. Literature review -- 3. Maximum penalized log-likelihood approach for proportional hazard model with partly interval-censored failure time data -- 4. Penalized likelihood methods for additive hazard model with partly interval-censored failure time data -- 5. Accelerated failure time (AFT) model with partly interval-censored failure time data -- 6. Conclusions and future work.
NotesBibliography: pages 147-153 Empirical thesis.
Awarding InstitutionMacquarie University
Degree TypeThesis PhD
DegreePhD, Macquarie University, Faculty of Science and Engineering, Department of Statistics
Department, Centre or SchoolDepartment of Statistics
Year of Award2015
Principal SupervisorJun Ma
RightsCopyright Jinqing Li 2014. Copyright disclaimer: http://www.copyright.mq.edu.au
Extent1 online resource (xii, 273 pages) graphs, tables
Former Identifiersmq:49166 http://hdl.handle.net/1959.14/1104655
maximum penalized likelihoodEstimation theoryadditive hazard modelcross validationproportional hazard modelprimal-dual interior point algorithmNewton-MI algorithminterval-censored failure time dataFailure time data analysisFailure time data analysis -- Proportional hazards modelsaccelerated failure time model