scholarly journals Logistic Discrimination using Robust Estimators

2006 ◽  
Author(s):  
Christophe Croux ◽  
Gentiane Haesbroeck ◽  
Kristel Joossens
Keyword(s):  
Robust Estimators ◽  
Logistic Discrimination

scholarly journals Logistic discrimination using robust estimators: An influence function approach

2008 ◽  
Vol 36 (1) ◽  
pp. 157-174 ◽  
Author(s):  
Christophe Croux ◽  
Gentiane Haesbroeck ◽  
Kristel Joossens
Keyword(s):  
Influence Function ◽  
Function Approach ◽  
Robust Estimators ◽  
Logistic Discrimination

Discriminant Analysis and its Applications in Epidemiology

1980 ◽  
Vol 19 (04) ◽  
pp. 220-226 ◽  
Author(s):  
P. A. Lachenbruch ◽  
W. R. Clarke
Keyword(s):  
Discriminant Analysis ◽  
Historical Review ◽  
Review Article ◽  
Logistic Discrimination

This review article discusses current use of discriminant analysis in epidemiology. Contents include historical review, simple extensions and generalizations, examples, evaluation of rules, logistic discrimination, and robustness.

Robust SURE estimates of profitability in the Egyptian insurance market

2021 ◽  
pp. 1-13
Author(s):  
Ahmed H. Youssef ◽  
Amr R. Kamel ◽  
Mohamed R. Abonazel
Keyword(s):  
Robust Regression ◽  
Ordinary Least Squares ◽  
Seemingly Unrelated Regression ◽  
Insurance Market ◽  
Insurance Companies ◽  
Regression Equations ◽  
Robust Estimators ◽  
Exogenous Variables ◽  
Net Profit ◽  
Mm Estimation

This paper proposed three robust estimators (M-estimation, S-estimation, and MM-estimation) for handling the problem of outlier values in seemingly unrelated regression equations (SURE) models. The SURE model is one of regression multivariate cases, which have especially assumption, i.e., correlation between errors on the multivariate linear models; by considering multiple regression equations that are linked by contemporaneously correlated disturbances. Moreover, the effects of outliers may permeate through the system of equations; the primary aim of SURE which is to achieve efficiency in estimation, but this is questionable. The goal of robust regression is to develop methods that are resistant to the possibility that one or several unknown outliers may occur anywhere in the data. In this paper, we study and compare the performance of robust estimations with the traditional non-robust (ordinary least squares and Zellner) estimations based on a real dataset of the Egyptian insurance market during the financial year from 1999 to 2018. In our study, we selected the three most important insurance companies in Egypt operating in the same field of insurance activity (personal and property insurance). The effect of some important indicators (exogenous variables) issued by insurance corporations on the net profit has been studied. The results showed that robust estimators greatly improved the efficiency of the SURE estimation, and the best robust estimation is MM-estimation. Moreover, the selected exogenous variables in our study have a significant effect on the net profit in the Egyptian insurance market.

scholarly journals Robust estimators for expression analysis

2002 ◽  
Vol 18 (12) ◽  
pp. 1585-1592 ◽  
Author(s):  
E. Hubbell ◽  
W.-M. Liu ◽  
R. Mei
Keyword(s):  
Expression Analysis ◽  
Robust Estimators

Robust Estimators of the 3-Parameter Weibull Distribution

1985 ◽  
Vol R-34 (4) ◽  
pp. 347-351 ◽  
Author(s):  
A. Adatia ◽  
L. K. Chan
Keyword(s):  
Weibull Distribution ◽  
Robust Estimators

Student's t Approximations for Three Simple Robust Estimators

1988 ◽  
Vol 83 (404) ◽  
pp. 1203 ◽  
Author(s):  
Kartik R. Patel ◽  
Govind S. Mudholkar ◽  
J. L. Indrasiri Fernando
Keyword(s):  
Robust Estimators ◽  
Student’S T

scholarly journals Imbalanced Learning Based on Logistic Discrimination

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Huaping Guo ◽  
Weimei Zhi ◽  
Hongbing Liu ◽  
Mingliang Xu
Keyword(s):  
Statistical Model ◽  
Cost Function ◽  
State Of The Art ◽  
Class Imbalance ◽  
Imbalanced Learning ◽  
Learning Problem ◽  
Logistic Discrimination ◽  
Positive Class ◽  
Negative Class ◽  
Novel Method

In recent years, imbalanced learning problem has attracted more and more attentions from both academia and industry, and the problem is concerned with the performance of learning algorithms in the presence of data with severe class distribution skews. In this paper, we apply the well-known statistical model logistic discrimination to this problem and propose a novel method to improve its performance. To fully consider the class imbalance, we design a new cost function which takes into account the accuracies of both positive class and negative class as well as the precision of positive class. Unlike traditional logistic discrimination, the proposed method learns its parameters by maximizing the proposed cost function. Experimental results show that, compared with other state-of-the-art methods, the proposed one shows significantly better performance on measures of recall,g-mean,f-measure, AUC, and accuracy.

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