The Performance of Robust Methods in Logistic Regression Model

Ahmed, Idriss Abdelmajid Idriss; Cheng, Weihu

doi:10.4236/ojs.2020.101010

Cited by 8 publications

(9 citation statements)

References 17 publications

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“…e goodness of fit of the model is 0.435, which shows that the model has good explanatory power. From the perspective of indicators, if the estimated coefficient of managers' risk preference, chairman and general manager's concurrent appointment, and management's shareholding ratio are positive, it indicates that managers' risk preference, chairman and general manager's concurrent appointment, and management's shareholding ratio are positively correlated with financial reporting fraud; if the estimated coefficient of directors' personnel replacement frequency, related party transaction frequency, and their influence is positive [25], it indicates that the risk preference of managers, chairman and general manager's concurrent appointment, and management's shareholding ratio are positively correlated with financial reporting fraud. If the estimated coefficient of the size of the regulatory council and the number of shareholders' meetings of the company is negative, then it is inversely proportional to the occurrence of fraud; if the estimated coefficient of the audit opinion type is negative, then it is inversely proportional to the occurrence of fraud; if the estimated coefficient of the current ratio and asset liability ratio is negative, then it is inversely proportional to the occurrence of fraud.…”

Section: Regression Analysis Of Prewarning and Postinvestigationmentioning

confidence: 99%

Identification of Accounting Fraud Based on Support Vector Machine and Logistic Regression Model

Qin

2021

Complexity

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The authenticity of the company’s accounting information is an important guarantee for the effective operation of the capital market. Accounting fraud is the tampering and distortion of the company’s public disclosure information. The continuous outbreak of fraud cases has dealt a heavy blow to the confidence of investors, shaken the credit foundation of the capital market, and hindered the healthy and stable development of the capital market. Therefore, it is of great theoretical and practical significance to carry out the research on the identification and governance of accounting fraud. Traditionally, accounting fraud identification is mostly based on linear thinking to build the fraud identification model. However, more and more studies show that fraud has typical nonlinear characteristics, and the multiobjective of fraud means also determines the limitations of using the linear model for identification. Considering that the traditional identification methods may have the defects of model setting error and insufficient information extraction, this paper constructs the support vector machine and logistic regression model to identify accounting fraud. The support vector machine is used to improve the learning ability and generalization ability of unknown phenomena, and the explanatory power of each variable to the whole model is identified by the logistic regression model. This paper breaks through the linear constraint hypothesis and explores the model setting form which is more suitable for the law of corporate fraud behaviour to extract the fraud identification information more fully and provide more powerful support for investors to effectively identify fraud.

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Section: Regression Analysis Of Prewarning and Postinvestigationmentioning

confidence: 99%

Identification of Accounting Fraud Based on Support Vector Machine and Logistic Regression Model

Qin

2021

Complexity

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“…8). However, in this study, parameters were estimated by equalizing the weights obtained by the weighting function introduced by [36] and proposed by [33]. First, the square of the Mahalanobis distances of the explanatory variables is calculated according to the computed 𝜇̂( 0) and Σ ̂(0) values.…”

Section: Weighted Maximum Likelihood Estimator (Wmle)mentioning

confidence: 99%

“…10). Weighted Bianco and Yohai (WBY) estimator can be defined as follows [27][28][29][30][31][32][33][34][35][36][37][38][39][40]:…”

Section: Bianco Yohai Estimator (Bye) and Weighted Bianco Yohai Estimator (Wbye)mentioning

confidence: 99%

“…After that, several robust alternative parameter estimation methods much less influenced by these points are suggested in the literature (i.e., [5,6,18,[21][22][23][24][25][26][27][28][29][30][31]). Besides, many researchers have also studied to compare the performances of the estimators to examine the robustness of these proposed estimators on simulation experiments [11,16,27,32,33]. These studies have shown that MLE can be influenced even by the presence of 1% IPs in the dataset, and therefore robust estimators were recommended [34].…”

Section: Introductionmentioning

confidence: 99%

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Robust Logistic Modelling for Datasets with Unusual Points

Tekin

Mestav

İyit

2021

Journal of New Theory

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Unusual Points (UPs) occur for different reasons, such as an observational error or the presence of a phenomenon with unknown cause. Influential Points (IPs), one of the UPs, have a negative effect on parameter estimation in the Logistic Regression model. Many researchers in fisheries sciences face this problem and have recourse to some manipulations to overcome this problem. The limitations of these manipulations have prompted researchers to use more suitable and innovative estimation techniques to deal with the problem. In this study, we examine the classification accuracies and parameter estimation performances of the Maximum Likelihood (ML) estimator and robust estimators through modified real datasets and simulation experiments. Besides, we discuss the potential applicability of the assessed robust estimators to the estimation models when the IPs are kept in the dataset. The obtained results show that the Weighted Maximum Likelihood (WML) and Weighted Bianco-Yohai (WBY) estimators of robust estimators outperform the others.

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“…15,16 This is due to its characteristics that LR classifier is appropriate for developing a probabilistic framework. 17,18 This is because it can easily adjust the classification threshold to find confidence classes. 18 LR classifier can easily incorporate new training data into the model as well.…”

Section: Introductionmentioning

confidence: 99%

Integration of logistic regression and multilayer perceptron for intelligent single and dual axis solar tracking systems

et al. 2021

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Intelligent solar tracking systems to track the trajectory of the sun across the sky has been actively studied and proposed nowadays. Several low performance intelligent solar tracking systems have been designed and implemented. Multilayer perceptron (MLP) is one of the common controllers that used to drive solar tracking systems. However, when the input data are complex for neural network, neural network would not well explain the relationship between these data. Thus, it performed worse than when the input data are simple. Using a premapping of relationship between samples of data as input to neural network along with the original input data could probably a strong guide to help neural network to reach the desired goal and predict the output variables faster and more accurate. It is found that using the output of logistic regression as input to neural network would faster the process of finding the predicted output by neural network. Thus, this study aims to propose new efficient and low complexity single and dual axis solar tracking systems by integrating supervised logistic regression (LR) and supervised MLP or cascade multilayer perceptron (CMLP). LR models are trained by using one of unsupervised clustering techniques (k-means, fuzzy c-means, and hierarchical clustering algorithms). The

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The Performance of Robust Methods in Logistic Regression Model

Cited by 8 publications

References 17 publications

Identification of Accounting Fraud Based on Support Vector Machine and Logistic Regression Model

Identification of Accounting Fraud Based on Support Vector Machine and Logistic Regression Model

Robust Logistic Modelling for Datasets with Unusual Points

Integration of logistic regression and multilayer perceptron for intelligent single and dual axis solar tracking systems

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