scholarly journals A novel technique for parameter estimation in intuitionistic fuzzy logistic regression model

2021 ◽  
Author(s):  
Abdullah Ali H. Ahmadini
Keyword(s):  
Logistic Regression ◽  
Parameter Estimation ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Novel Technique ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Logistic Regression

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.

Intuitionistic Fuzzy Partial Logistic Regression Model Using Ridge Methodology

2020 ◽  
Vol 28 (04) ◽  
pp. 527-543
Author(s):  
Gholamreza Hesamian ◽  
Mohammad Ghasem Akbari ◽  
Mehdi Roozbeh
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Smooth Function ◽  
Potential Application ◽  
Goodness Of Fit ◽  
Logistic Regression Model ◽  
Numerical Examples ◽  
Ridge Estimation ◽  
Intuitionistic Fuzzy

This paper applies a ridge estimation approach in an existing partial logistic regression model with exact predictors, intuitionistic fuzzy responses, intuitionistic fuzzy coefficients and intuitionistic fuzzy smooth function to improve an existing intuitionistic fuzzy partial logistic regression model in the presence of multicollinearity. For this purpose, ridge methodology is also involved to estimate the parametric intuitionistic fuzzy coefficients and nonparametric intuitionistic fuzzy smooth function. Some common goodness-of-fit criteria are also used to examine the performance of the proposed regression model. The potential application of the proposed method are illustrated and compared with the intuitionistic partial logistic regression model through two numerical examples. The results clearly indicate the proposed ridge method is quite efficient in model’s performances when there is multicollinearity among the predictors.

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