Bayesian Inference for Logit-Model using Informative and Non-informative Priors

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ID: 316224
2014
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Abstract
In the field of econometrics analysis of binary data is widely done. When the sample is small Bayesian approach provides more appropriate results on classical approach (MLE). In Bayesian approach the results can be improved by using different Priors. It is known that the binary data can be modeled by using Logistic, Probit or Tobit Links. In our study, we use Logistic Link. When modeling binary data, the shape of the distribution of Regression coefficients is no more normal. To find the coefficient of Skewness of Regression coefficients we use different Priors. It is observed that Haldane Prior provides better results than Jeffreys Prior, while Informative Prior performs better than the other Non-informative Priors for the data set under consideration.
Reference Key
imported_1780934265_6a26e679d7185 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Tahir Abbas Malik, Muhammad Aslam
Journal Journal of Statistics
Year 2014
DOI
DOI not found
URL
https://jstat.gcu.edu.pk/index.php/jstat/article/view/87
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