EconPapers    
Economics at your fingertips  
 

A study on the effect of the loss function on Bayesian estimation and posterior risk of binomial distribution

Ming Han

Communications in Statistics - Theory and Methods, 2021, vol. 50, issue 18, 4386-4399

Abstract: The effect of different loss functions on Bayesian estimation and its posterior risk is studied in this paper. The definition of quasi-modified squared error loss function is proposed based on the modified squared error loss function, and the formulas of Bayesian estimation and its posterior risk under the quasi-modified squared error loss function are developed, respectively. Moreover, the Bayesian estimations and its posterior risks of binomial distribution parameter under different loss functions are introduced. Monte Carlo simulation and application examples are provided for illustrative purpose, and the results are compared on the basis of posterior risk and mean square error (MSE). Finally, the Bayesian estimations and the Bayesian credible intervals are obtained, respectively, by MCMC method (also using OpenBUGS).

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2020.1719160 (text/html)
Access to full text is restricted to subscribers.

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:taf:lstaxx:v:50:y:2021:i:18:p:4386-4399

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2020.1719160

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().

 
Page updated 2025-03-20
Handle: RePEc:taf:lstaxx:v:50:y:2021:i:18:p:4386-4399