EconPapers    
Economics at your fingertips  
 

A Bayesian approach for multi-stage models with linear time-dependent hazard rate

Pham Hoa () and Pham Huong T. T. ()
Additional contact information
Pham Hoa: Mathematical Department, An Giang University; and Vietnam National University, Ho Chi Minh City, Vietnam
Pham Huong T. T.: Mathematical Department, An Giang University; and Vietnam National University, Ho Chi Minh City, Vietnam

Monte Carlo Methods and Applications, 2019, vol. 25, issue 4, 307-316

Abstract: Multi-stage models have been used to describe progression of individuals which develop through a sequence of discrete stages. We focus on the multi-stage model in which the number of individuals in each stage is assessed through destructive samples for a sequence of sampling time. Moreover, the stage duration distributions of the model are effected by a time-dependent hazard rate. The multi-stage models become complex with a stage having time-dependent hazard rate. The main aim of this paper is to derive analytically the approximation of the likelihood of the model. We apply the approximation to the Metropolis–Hastings (MH) algorithm to estimate parameters for the model. The method is demonstrated by applying to simulated data which combine non-hazard rate, stage-wise constant hazard rate and time-dependent hazard rates in stage duration distributions.

Keywords: Multi-stage models; stage duration; stage frequency data; Bayesian analysis; destructive samples; 62N02; 62F15; 62P10; 62P12 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
https://doi.org/10.1515/mcma-2019-2051 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:mcmeap:v:25:y:2019:i:4:p:307-316:n:4

Ordering information: This journal article can be ordered from
https://www.degruyter.com/view/j/mcma

DOI: 10.1515/mcma-2019-2051

Access Statistics for this article

Monte Carlo Methods and Applications is currently edited by Karl K. Sabelfeld

More articles in Monte Carlo Methods and Applications from De Gruyter
Bibliographic data for series maintained by Peter Golla ().

 
Page updated 2021-06-12
Handle: RePEc:bpj:mcmeap:v:25:y:2019:i:4:p:307-316:n:4