IDEAS home Printed from https://ideas.repec.org/p/sek/iacpro/6409196.html
   My bibliography  Save this paper

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

Author

Listed:
  • Autcha Araveeporn

    () (King Mongkut?s Institute of Technology Ladkrabang)

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.

Suggested Citation

  • Autcha Araveeporn, 2018. "A Comparison of Parameter Estimation of Logistic Regression model by Maximum Likelihood, Ridge Regression, Markov Chain Monte Carlo Methods," Proceedings of International Academic Conferences 6409196, International Institute of Social and Economic Sciences.
  • Handle: RePEc:sek:iacpro:6409196
    as

    Download full text from publisher

    File URL: https://iises.net/proceedings/35th-international-academic-conference-barcelona-spain/table-of-content/detail?cid=64&iid=004&rid=9196
    File Function: First version, 2018
    Download Restriction: no

    More about this item

    Keywords

    Maximum Likelihood; Ridge Regression; Markov Chain Monte Carlo;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sek:iacpro:6409196. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Klara Cermakova). General contact details of provider: https://iises.net/ .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.