EconPapers    
Economics at your fingertips  
 

Robust estimation with variational Bayes in presence of competing risks

Himanshu Rai (), Sanjeev K. Tomer () and Anoop Chaturvedi ()
Additional contact information
Himanshu Rai: Banaras Hindu University
Sanjeev K. Tomer: Banaras Hindu University

METRON, 2021, vol. 79, issue 2, No 6, 207-223

Abstract: Abstract Variational Bayes, a method from machine learning, can provide a good approximation to the intractable posterior density function. It converges fast and works efficiently for large data sets. In this paper, we employ this method for robust Bayesian estimation of cause-specific quantities using competing risk data with missing causes. We consider the contamination class of prior distributions for the concerned parameter and discuss the implementation of ML-II procedure of Good (Good thinking: the foundations of probability and its applications, University of Minnesota Press, Minnesota , 1983) through variational Bayes approach in order to select a prior in a data-dependent fashion leading to a robust posterior. We perform sensitivity analysis to observe the influence of prior on some posterior quantities of interest. We analyze a real data set of computer hard-drives having three competing causes of failure and illustrate that the considered method provides robust Bayes estimates of concerned parameters, cause-specific hazard, and cumulative incidence function.

Keywords: Competing risk; $$\varepsilon $$ ε -Contamination class of prior; Variational Bayes; ML-II procedure; Prior influence; Robust inference (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://link.springer.com/10.1007/s40300-021-00208-7 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:metron:v:79:y:2021:i:2:d:10.1007_s40300-021-00208-7

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/40300

DOI: 10.1007/s40300-021-00208-7

Access Statistics for this article

METRON is currently edited by Marco Alfo'

More articles in METRON from Springer, Sapienza Università di Roma
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2023-01-23
Handle: RePEc:spr:metron:v:79:y:2021:i:2:d:10.1007_s40300-021-00208-7