Weighted Pseudometric Discriminatory Power Improvement Using a Bayesian Logistic Regression Model Based on a Variational Method

2008 ◽  
Vol 30 (2) ◽  
pp. 253-266 ◽  
Author(s):  
R. Ksantini ◽  
D. Ziou ◽  
B. Colin ◽  
F. Dubeau
Keyword(s):  
Logistic Regression ◽  
Variational Method ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Discriminatory Power ◽  
Model Based ◽  
Bayesian Logistic Regression

An Optimized Logistic Regression Model Based on the Maximum Entropy Estimation Under the Hesitant Fuzzy Environment

2021 ◽  
pp. 1-25
Author(s):  
Chenyang Song ◽  
Liguo Wang ◽  
Zeshui Xu
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Maximum Entropy ◽  
Logistic Regression Model ◽  
Uncertain Information ◽  
Entropy Estimation ◽  
Model Based ◽  
Fuzzy Environment ◽  
Maximum Entropy Estimation ◽  
Specific Implementation

The logistic regression model is one of the most widely used classification models. In some practical situations, few samples and massive uncertain information bring more challenges to the application of the traditional logistic regression. This paper takes advantages of the hesitant fuzzy set (HFS) in depicting uncertain information and develops the logistic regression model under hesitant fuzzy environment. Considering the complexity and uncertainty in the application of this logistic regression, the concept of hesitant fuzzy information flow (HFIF) and the correlation coefficient between HFSs are introduced to determine the main factors. In order to better manage situations with small samples, a new optimized method based on the maximum entropy estimation is also proposed to determine the parameters. Then the Levenberg–Marquardt Algorithm (LMA) under hesitant fuzzy environment is developed to solve the parameter estimation problem with fewer samples and uncertain information in the logistic regression model. A specific implementation process for the optimized logistic regression model based on the maximum entropy estimation under the hesitant fuzzy environment is also provided. Moreover, we apply the proposed model to the prediction problem of Emergency Extreme Air Pollution Event (EEAPE). A comparative analysis and a sensitivity analysis are further conducted to illustrate the advantages of the optimized logistic regression model under hesitant fuzzy environment.

scholarly journals A Logistic Regression Model Based on the National Mammography Database Format to Aid Breast Cancer Diagnosis

2009 ◽  
Vol 192 (4) ◽  
pp. 1117-1127 ◽  
Author(s):  
Jagpreet Chhatwal ◽  
Oguzhan Alagoz ◽  
Mary J. Lindstrom ◽  
Charles E. Kahn ◽  
Katherine A. Shaffer ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Logistic Regression ◽  
Regression Model ◽  
Cancer Diagnosis ◽  
Logistic Regression Model ◽  
Breast Cancer Diagnosis ◽  
Model Based

Evaluation of the financial condition of companies after the announcement of arrangement bankruptcy: application of the classical and Bayesian logistic regression

2020 ◽  
Vol 67 (1) ◽  
pp. 5-32
Author(s):  
Barbara Pawełek ◽  
Jadwiga Kostrzewska ◽  
Maciej Kostrzewski ◽  
Krzysztof Gałuszka
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Construction Industry ◽  
Bayesian Approach ◽  
Logistic Regression Model ◽  
Likelihood Method ◽  
Logistic Regression Models ◽  
Financial Condition ◽  
Bayesian Logistic Regression ◽  
The Bayesian Approach

The aim of this paper is to present the results of an assessment of the financial condition of companies from the construction industry after the announcement of arrangement bankruptcy, in comparison to the condition of healthy companies. The logistic regression model estimated by means of the maximum likelihood method and the Bayesian approach were used. The first achievement of our study is the assessment of the financial condition of companies from the construction industry after the announcement of bankruptcy. The second achievement is the application of an approach combining the classical and Bayesian logistic regression models to assess the financial condition of companies in the years following the declaration of bankruptcy, and the presentation of the benefits of such a combination. The analysis described in the paper, carried out in most part by means of the ML logistic regression model, was supplemented with information yielded by the application of the Bayesian approach. In particular, the analysis of the shape of the posterior distribution of the repeat bankruptcy probability makes it possible, in some cases, to observe that the financial condition of a company is not clear, despite clear assessments made on the basis of the point estimations.

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