scholarly journals Stochastic Restricted Maximum Likelihood Estimator in Logistic Regression Model

2015 ◽  
Vol 05 (07) ◽  
pp. 837-851 ◽  
Author(s):  
Varathan Nagarajah ◽  
Pushpakanthie Wijekoon
Keyword(s):  
Logistic Regression ◽  
Maximum Likelihood ◽  
Regression Model ◽  
Maximum Likelihood Estimator ◽  
Logistic Regression Model ◽  
Restricted Maximum Likelihood ◽  
Likelihood Estimator ◽  
Restricted Maximum Likelihood Estimator

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

2018 ◽  
Vol 48 (3) ◽  
pp. 199-204 ◽  
Author(s):  
R. LI ◽  
J. ZHOU ◽  
L. WANG
Keyword(s):  
Logistic Regression ◽  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Binary Logistic Regression ◽  
Parametric Bootstrap ◽  
Binary Logistic Regression Model ◽  
Bayesian Bootstrap ◽  
Non Parametric

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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