A Kernel Smooth Approach for Joint Modeling of Accelerated Failure Time and Longitudinal Data

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/105684

Title:	A Kernel Smooth Approach for Joint Modeling of Accelerated Failure Time and Longitudinal Data
Authors:	曾議寬;Tseng, Y. K.;Yang, Y. F.
Contributors:	理學院統計研究所
Keywords:	AFT model;Computer simulation;EM algorithm;Failure times;Hazard smoothing;Joint model;Kernels;Markov analysis;Mathematical analysis;Mathematical models;Maximization;Monte Carlo simulation;Search methods;Step functions
Date:	2016-04-20
Issue Date:	2026-04-23 12:47:21 (UTC+8)
Publisher:	Taylor and Francis Ltd.;Philadelphia: Taylor & Francis
Abstract:	摘要： Joint likelihood approaches have been widely used to handle survival data with time-dependent covariates. In construction of the joint likelihood function for the accelerated failure time (AFT) model, the unspecified baseline hazard function is assumed to be a piecewise constant function in the literature. However, there are usually no close form formulas for the regression parameters, which require numerical methods in the EM iterations. The nonsmooth step function assumption leads to very spiky likelihood function which is very hard to find the globe maximum. Besides, due to nonsmoothness of the likelihood function, direct search methods are conducted for the maximization which are very inefficient and time consuming. To overcome the two disadvantages, we propose a kernel smooth pseudo-likelihood function to replace the nonsmooth step function assumption. The performance of the proposed method is evaluated by simulation studies. A case study of reproductive egg-laying data is provided to demonstrate the usefulness of the new approach. 出版者： Philadelphia: Taylor & Francis 出版日期： 2016-04-20 出處： Communications in statistics. Simulation and computation, 2016-04, Vol.45 (4), p.1240-1248 版權： Copyright © Taylor & Francis Group, LLC 2016 版權： Copyright © Taylor & Francis Group, LLC 識別號： ISSN: 0361-0918 識別號： EISSN: 1532-4141 識別號： DOI: 10.1080/03610918.2013.809099
Appears in Collections:	[Graduate Institute of Statistics] journal & Dissertation

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