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Journal article 1227 views

Quantile function models for survival data analysis

Yuzhi Cai Orcid Logo

Australian and New Zealand Journal of Statistics, Volume: 55, Pages: 155 - 172

Swansea University Author: Yuzhi Cai Orcid Logo

Abstract

In this paper we propose a quantile survival model to analyze censored data. Thisapproach provides a very effective way to construct a proper model for the survival timeconditional on some covariates. Once a quantile survival model for the censored data isestablished, the survival density, survival...

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Published in: Australian and New Zealand Journal of Statistics
Published: 2013
URI: https://cronfa.swan.ac.uk/Record/cronfa15288
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first_indexed 2013-08-22T01:57:36Z
last_indexed 2018-02-09T04:47:07Z
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spelling 2016-10-31T11:00:53.6109015 v2 15288 2013-07-30 Quantile function models for survival data analysis eff7b8626ab4cc6428eef52516fda7d6 0000-0003-3509-9787 Yuzhi Cai Yuzhi Cai true false 2013-07-30 BAF In this paper we propose a quantile survival model to analyze censored data. Thisapproach provides a very effective way to construct a proper model for the survival timeconditional on some covariates. Once a quantile survival model for the censored data isestablished, the survival density, survival or hazard functions of the survival time can beobtained easily. For illustration purposes, we focus on a model that is based on thegeneralized lambda distribution (GLD). The GLD and many other quantile functionmodels are defined only through their quantile functions, no closed-form expressions areavailable for other equivalent functions. We also develop a Bayesian Markov ChainMonte Carlo (MCMC) method for parameter estimation. Extensive simulation studieshave been conducted. Both simulation study and application results show that theproposed quantile survival models can be very useful in practice. Journal Article Australian and New Zealand Journal of Statistics 55 155 172 Bayesian method; generalized lambda distribution; survival function; survival data; quantile function 30 6 2013 2013-06-30 COLLEGE NANME Accounting and Finance COLLEGE CODE BAF Swansea University 2016-10-31T11:00:53.6109015 2013-07-30T10:09:05.8811651 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Yuzhi Cai 0000-0003-3509-9787 1
title Quantile function models for survival data analysis
spellingShingle Quantile function models for survival data analysis
Yuzhi Cai
title_short Quantile function models for survival data analysis
title_full Quantile function models for survival data analysis
title_fullStr Quantile function models for survival data analysis
title_full_unstemmed Quantile function models for survival data analysis
title_sort Quantile function models for survival data analysis
author_id_str_mv eff7b8626ab4cc6428eef52516fda7d6
author_id_fullname_str_mv eff7b8626ab4cc6428eef52516fda7d6_***_Yuzhi Cai
author Yuzhi Cai
author2 Yuzhi Cai
format Journal article
container_title Australian and New Zealand Journal of Statistics
container_volume 55
container_start_page 155
publishDate 2013
institution Swansea University
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
document_store_str 0
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description In this paper we propose a quantile survival model to analyze censored data. Thisapproach provides a very effective way to construct a proper model for the survival timeconditional on some covariates. Once a quantile survival model for the censored data isestablished, the survival density, survival or hazard functions of the survival time can beobtained easily. For illustration purposes, we focus on a model that is based on thegeneralized lambda distribution (GLD). The GLD and many other quantile functionmodels are defined only through their quantile functions, no closed-form expressions areavailable for other equivalent functions. We also develop a Bayesian Markov ChainMonte Carlo (MCMC) method for parameter estimation. Extensive simulation studieshave been conducted. Both simulation study and application results show that theproposed quantile survival models can be very useful in practice.
published_date 2013-06-30T03:17:25Z
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score 11.016392

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