Semiparametric sieve maximum likelihood estimation for interval censored data with/without cure fraction

This thesis focuses on semiparametric sieve maximum likelihood esti- mation of interval censored survival data. It consists of two parts. In the first part, we discuss the accelerated hazards (AH) model, which provides an alternative to the popular proportional hazards (PH) model when the proportio...

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Main Author: Szabo, Zsolt
Other Authors: Xiang Liming
Format: Theses and Dissertations
Language:English
Published: 2018
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Online Access:http://hdl.handle.net/10356/75048
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-750482023-02-28T23:56:07Z Semiparametric sieve maximum likelihood estimation for interval censored data with/without cure fraction Szabo, Zsolt Xiang Liming School of Physical and Mathematical Sciences DRNTU::Science::Mathematics::Statistics This thesis focuses on semiparametric sieve maximum likelihood esti- mation of interval censored survival data. It consists of two parts. In the first part, we discuss the accelerated hazards (AH) model, which provides an alternative to the popular proportional hazards (PH) model when the proportionality does not hold. We explore the difficulties that arise when one fits the AH model to interval censored data. We develop a semiparametric sieve maximum likelihood estimator and provide an algorithm for its implementation. We also establish consistency results and set up the rate of convergence. In the second part, we propose a new double semiparametric mixture cure model for analyzing interval censored data with possible cure frac- tion. The proposed model incorporates semiparametric latency and inci- dence parts. Unlike existing works in the literature, where the incidence follows a parametric model, the proposed model allows the incidence to be semiparametric. We develop a spline-based sieve maximum likelihood es- timation approach to analyze such data. Using modern empirical process techniques we establish large sample properties of the estimator, including the consistency, rate of convergence and the asymptotic normality of the finite dimensional parameters. For both parts of the thesis we provide extensive simulation studies to show the finite sample size performance of the proposed estimation algorithm. For illustration purpose, the proposed method are applied to real data. ​Doctor of Philosophy (SPMS) 2018-05-28T02:23:17Z 2018-05-28T02:23:17Z 2018 Thesis Szabo, Z. (2018). Semiparametric sieve maximum likelihood estimation for interval censored data with/without cure fraction. Doctoral thesis, Nanyang Technological University, Singapore. http://hdl.handle.net/10356/75048 10.32657/10356/75048 en 109 p. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic DRNTU::Science::Mathematics::Statistics
spellingShingle DRNTU::Science::Mathematics::Statistics
Szabo, Zsolt
Semiparametric sieve maximum likelihood estimation for interval censored data with/without cure fraction
description This thesis focuses on semiparametric sieve maximum likelihood esti- mation of interval censored survival data. It consists of two parts. In the first part, we discuss the accelerated hazards (AH) model, which provides an alternative to the popular proportional hazards (PH) model when the proportionality does not hold. We explore the difficulties that arise when one fits the AH model to interval censored data. We develop a semiparametric sieve maximum likelihood estimator and provide an algorithm for its implementation. We also establish consistency results and set up the rate of convergence. In the second part, we propose a new double semiparametric mixture cure model for analyzing interval censored data with possible cure frac- tion. The proposed model incorporates semiparametric latency and inci- dence parts. Unlike existing works in the literature, where the incidence follows a parametric model, the proposed model allows the incidence to be semiparametric. We develop a spline-based sieve maximum likelihood es- timation approach to analyze such data. Using modern empirical process techniques we establish large sample properties of the estimator, including the consistency, rate of convergence and the asymptotic normality of the finite dimensional parameters. For both parts of the thesis we provide extensive simulation studies to show the finite sample size performance of the proposed estimation algorithm. For illustration purpose, the proposed method are applied to real data.
author2 Xiang Liming
author_facet Xiang Liming
Szabo, Zsolt
format Theses and Dissertations
author Szabo, Zsolt
author_sort Szabo, Zsolt
title Semiparametric sieve maximum likelihood estimation for interval censored data with/without cure fraction
title_short Semiparametric sieve maximum likelihood estimation for interval censored data with/without cure fraction
title_full Semiparametric sieve maximum likelihood estimation for interval censored data with/without cure fraction
title_fullStr Semiparametric sieve maximum likelihood estimation for interval censored data with/without cure fraction
title_full_unstemmed Semiparametric sieve maximum likelihood estimation for interval censored data with/without cure fraction
title_sort semiparametric sieve maximum likelihood estimation for interval censored data with/without cure fraction
publishDate 2018
url http://hdl.handle.net/10356/75048
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