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...
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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 |
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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 |
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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. |
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LI, Yong LIU, Xiao-Bin YU, Jun |
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LI, Yong LIU, Xiao-Bin YU, Jun |
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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 |
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A Bayesian Chi-Squared Test for Hypothesis Testing |
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A Bayesian Chi-Squared Test for Hypothesis Testing |
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bayesian chi-squared test for hypothesis testing |
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Institutional Knowledge at Singapore Management University |
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2014 |
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https://ink.library.smu.edu.sg/soe_research/1588 https://ink.library.smu.edu.sg/context/soe_research/article/2587/viewcontent/03_2014.pdf |
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