ESTIMATION OF THE BINARY LOGISTIC REGRESSION MODEL PARAMETER USING BOOTSTRAP RE-SAMPLING

Authors

  • R. LI
  • J. ZHOU
  • L. WANG

DOI:

https://doi.org/10.52292/j.laar.2018.228

Keywords:

Non-parametric bootstrap, Non-parametric Bayesian bootstrap;, Logistic Regression, Confidence Interval, Parameter Estimation

Abstract

In this paper, the non-parametric bootstrap and non-parametric Bayesian bootstrap methods are applied for parameter estimation in the binary logistic regression model. A real data study and a simulation study are conducted to compare the Nonparametric bootstrap, Non-parametric Bayesian bootstrap and the maximum likelihood methods. Study results shows that three methods are all effective ways for parameter estimation in the binary logistic regression model. In small sample case, the non-parametric Bayesian bootstrap method performs relatively better than the non-parametric bootstrap and the maximum likelihood method for parameter estimation in the binary logistic regression model.

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Published

2018-07-31

Issue

Vol. 48 No. 3 (2018): Latin American Applied Research

Section

Control and Information Processing

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