A Bayesian Chi-Squared Test for Hypothesis Testing

A new Bayesian test statistic is proposed to test a point null hypothesis based on of regular conditions and follows a chi-squared distribution when the null hypothesis is correct. The new statistic has several important advantages that make it appeal in practical applications. First, it is well-def...

Full description

Saved in:
Bibliographic Details
Main Authors: LI, Yong, LIU, Xiao-Bin, YU, Jun
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2014
Subjects:
Online Access:https://ink.library.smu.edu.sg/soe_research/1588
https://ink.library.smu.edu.sg/context/soe_research/article/2587/viewcontent/03_2014.pdf
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Singapore Management University
Language: English
id sg-smu-ink.soe_research-2587
record_format dspace
spelling sg-smu-ink.soe_research-25872019-04-19T08:40:52Z A Bayesian Chi-Squared Test for Hypothesis Testing LI, Yong LIU, Xiao-Bin YU, Jun A new Bayesian test statistic is proposed to test a point null hypothesis based on of regular conditions and follows a chi-squared distribution when the null hypothesis is correct. The new statistic has several important advantages that make it appeal in practical applications. First, it is well-defined under improper prior distributions. Second, it avoids Jeffrey-Lindley’s paradox. Third, it is relatively easy to compute, even for models with latent variables. Finally, it is pivotal and its threshold value can be easily obtained from the asymptotic chi-squared distribution. The method is illustrated using some real examples in economics and finance. 2014-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/soe_research/1588 https://ink.library.smu.edu.sg/context/soe_research/article/2587/viewcontent/03_2014.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Bayes factor; Decision theory; EM algorithm; Lagrange multiplier; Markov chain Monte Carlo; Latent variable models. Econometrics
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Bayes factor; Decision theory; EM algorithm; Lagrange multiplier; Markov chain Monte Carlo; Latent variable models.
Econometrics
spellingShingle Bayes factor; Decision theory; EM algorithm; Lagrange multiplier; Markov chain Monte Carlo; Latent variable models.
Econometrics
LI, Yong
LIU, Xiao-Bin
YU, Jun
A Bayesian Chi-Squared Test for Hypothesis Testing
description A new Bayesian test statistic is proposed to test a point null hypothesis based on of regular conditions and follows a chi-squared distribution when the null hypothesis is correct. The new statistic has several important advantages that make it appeal in practical applications. First, it is well-defined under improper prior distributions. Second, it avoids Jeffrey-Lindley’s paradox. Third, it is relatively easy to compute, even for models with latent variables. Finally, it is pivotal and its threshold value can be easily obtained from the asymptotic chi-squared distribution. The method is illustrated using some real examples in economics and finance.
format text
author LI, Yong
LIU, Xiao-Bin
YU, Jun
author_facet LI, Yong
LIU, Xiao-Bin
YU, Jun
author_sort LI, Yong
title A Bayesian Chi-Squared Test for Hypothesis Testing
title_short A Bayesian Chi-Squared Test for Hypothesis Testing
title_full A Bayesian Chi-Squared Test for Hypothesis Testing
title_fullStr A Bayesian Chi-Squared Test for Hypothesis Testing
title_full_unstemmed A Bayesian Chi-Squared Test for Hypothesis Testing
title_sort bayesian chi-squared test for hypothesis testing
publisher Institutional Knowledge at Singapore Management University
publishDate 2014
url https://ink.library.smu.edu.sg/soe_research/1588
https://ink.library.smu.edu.sg/context/soe_research/article/2587/viewcontent/03_2014.pdf
_version_ 1770571947963318272