A Comparison of Parameter Estimation of Logistic Regression model by Maximum Likelihood, Ridge Regression, Markov Chain Monte Carlo Methods

Autcha Araveeporn ()
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Autcha Araveeporn: King Mongkut?s Institute of Technology Ladkrabang

No 6409196, Proceedings of International Academic Conferences from International Institute of Social and Economic Sciences

Abstract: The goal of this research is to estimate the parameter of logistic regression model. The coefficient parameter is evaluated by maximum likelihood, ridge regression, markov chain monte carlo methods. The logistic regression is considered the correlation between binary dependent variable and 2, 3, and 4 independent variables which is generated from normal distribution, contaminated normal distribution, and t distribution. The maximum likelihood estimator is estimated by differential the log likelihood function with respect to the coefficients. Ridge regression is to choose the unknown ridge parameter by cross-validation, so ridge estimator is evaluated on a form of maximum likelihood method by adding ridge parameter. The markov chain monte carlo estimator can approximate from Gibbs sampling algorithm by the posterior distribution based on a probability distribution and prior probability distribution. The performance of these method is compare by percentage of predicted accuracy value. The results are found that ridge regression are satisfied when the independent variables are simulated from normal distribution, and the maximum likelihood outperforms on the other distributions.

Keywords: Maximum Likelihood; Ridge Regression; Markov Chain Monte Carlo (search for similar items in EconPapers)
JEL-codes: C13 C15 (search for similar items in EconPapers)
Pages: 9 pages
Date: 2018-06
New Economics Papers: this item is included in nep-dcm, nep-ecm and nep-ore
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Published in Proceedings of the Proceedings of the 35th International Academic Conference, Barcelona, Jun 2018, pages 4-12

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Persistent link: https://EconPapers.repec.org/RePEc:sek:iacpro:6409196

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