scholarly journals Comparison between normal weights and conditional Bays weights in Iterative principal component estimators

2018 ◽  
Vol 24 (109) ◽  
pp. 535
Author(s):  
اياد حبيب شمال
Keyword(s):  
Logistic Regression ◽  
Renal Disease ◽  
Regression Model ◽  
Nonlinear Regression ◽  
Logistic Regression Model ◽  
Principal Component ◽  
Binary Response ◽  
Nonlinear Regression Model ◽  
Qualitative Variable ◽  
Mean Error

Abstract: This paper discusses the problem of semi maulticollinearity in the nonlinear regression model (the multi-logistic regression model) When the dependent variable is a qualitative variable, the binary response is either equal to one for a response or zero for no response, Through the use of Iterative principal component estimatorsWhich are based on the normal weights and conditional Bays weights . If the appliede Estimates this model Through the use of two types of drugs concentrations thy concentration of ciprodar (variable X1) On a number of people with Patients with renal disease represent the dependent variable (The person heals from the disease  , The person has not recovered from the disease )from through Mean Error Squares (MSE) The results were indicative of Iterative principal component estemaite   Depending on the conditional Bays weights prefer the Iterative principal component estimators Depending on the the normal weights.

The prediction algorithm of credit risk of science and technology finance based on cloud computing

2021 ◽  
pp. 1-17
Author(s):  
Guiping Li
Keyword(s):  
Cloud Computing ◽  
Logistic Regression ◽  
Credit Risk ◽  
Regression Model ◽  
Risk Prediction ◽  
Logistic Regression Model ◽  
Science And Technology ◽  
Principal Component ◽  
Prediction Algorithm ◽  
Financial Indicators

In order to effectively guarantee the effect of credit risk prediction of science and technology finance and improve the ability of risk prediction, a credit risk prediction algorithm of science and technology finance based on cloud computing is proposed. The logistic regression model is used to predict, and the financial indicators of science and technology credit are selected as the model covariates. According to the characteristics and strong correlation of many financial indicators of science and technology credit, this paper constructs the final index system of online supply chain technology credit risk evaluation based on SMEs. Then the principal component analysis method is used to select the principal component. Combined with the penalty method, the data space dimension of financial indicators is further reduced, and the unrelated principal components are obtained. On this basis, a logistic regression model is established to predict the credit risk by taking the selected main components as covariates. The experimental results show that the algorithm has a good fit to the credit risk of 16 science and technology credit enterprises, and the risk prediction ability is significantly improved, which can effectively guarantee the effect of science and technology credit risk prediction.

scholarly journals Utilizing the Logistic Regression Model in Analyzing the Categorical Data of Economic Effects

2021 ◽  
Vol 12 (4) ◽  
pp. 638-646
Author(s):  
Mahdi Wahhab Neamah, Et. al.
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Categorical Data ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Ordinary Least Squares ◽  
Economic Effects ◽  
Ordered Categories ◽  
Qualitative Variable ◽  
Qualitative Variables

The categorical data has a significant role in representing statistical binary variables, and they are analyzed by means of grouping the response variable into ordered categories. Thereby, the dependent variable becomes of type binary qualitative variable. The data related to the financial position of world countries is classified within the categorical data. This work is to study the economic effects of an individual's different factors on determining the richness or poorness levels of a selected population of countries. Moreover, a logistic regression model is to be created to estimate these levels. As a sample of research, the categorical data relevant to the financial status of 20 Arabic countries were drawn from the website of the World Bank, WB. In addition, for comparison purpose, another similar set of categorical data was generated by MATLAB too. The paper has been based on two hypotheses, first is the well-known regression models, like the ordinary least squares or maximum likelihood, are not accurate in case of binary qualitative variables. Second, is utilizing the logistic regression model as an alternative model to achieve the paper goal.  The paper results, for both WB data and MATLAB data, have successfully proved the ability of the logistic regression model in manipulating the categorical data and predicting the coefficients of the corresponding regression models.   

scholarly journals Robust logistic regression in the presence of high leverage points

2019 ◽  
Vol 11 (3) ◽  
Author(s):  
Mohammed A. Mohammed
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Gradient Descent ◽  
Logistic Regression Model ◽  
Stochastic Gradient Descent ◽  
Binary Response ◽  
Suggested Approach ◽  
Leverage Points ◽  
M Estimator ◽  
Minimum Volume Ellipsoid

In this article we conceder the logistic regression model with high leverage points. For the logistic regression model with a binary response, we suggested a new robust approach called robust logistic regression (RLR) based on the robust mahalanobis distance (RMD) which depends on the minimum volume ellipsoid (MVE) estimators. The RMD is computed by using the algorithm of stochastic gradient descent (SGD). In order to assist the new suggested approach we compare it with some existing method such as maximum likelihood estimator and robust M-estimator in logistic regression model. The simulation study points that the RLR has supreme performances throw some measurement comparison.

Application of Principal Component Analysis and Logistic Regression Model in Lupus Nephritis Patients with Clinical Hypothyroidism

2020 ◽  
Author(s):  
Ting Huang ◽  
Jiarong Li ◽  
Weiru Zhang
Keyword(s):  
Risk Factors ◽  
Principal Component Analysis ◽  
Logistic Regression ◽  
Lupus Nephritis ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Area Under The Curve ◽  
Principal Component ◽  
Component Analysis ◽  
Cross Sectional

Abstract Background : Previous studies indicate that the prevalence of hypothyroidism is much higher in patients with lupus nephritis (LN) than in the general population, and is associated with LN’s activity. Principal component analysis (PCA) and logistic regression can help determine relevant risk factors and identify LN patients at high risk of hypothyroidism; as such, these tools may prove useful in managing this disease. Methods: We carried out a cross-sectional study of 143 LN patients diagnosed by renal biopsy, all of whom had been admitted to Xiangya Hospital of Central South University in Changsha, China, between June 2012 and December 2016. The PCA–logistic regression model was used to determine the influential principal components for LN patients who have hypothyroidism. Results : Our PCA–logistic regression analysis results demonstrated that serum creatinine, blood urea nitrogen, blood uric acid, total protein, albumin, and anti-ribonucleoprotein antibody were important clinical variables for LN patients with hypothyroidism. The area under the curve of this model was 0.855. Conclusion : The PCA–logistic regression model performed well in identifying important risk factors for certain clinical outcomes, and promoting clinical research on other diseases will be beneficial. Using this model, clinicians can identify at-risk subjects and either implement preventative strategies or manage current treatments.

A case study of Stroke patients in Senegal: application of Generalized extreme value regression model

2021 ◽  
Vol 8 (1) ◽  
pp. 1497-1506
Author(s):  
Aba Dio ◽  
El Hadji Dème ◽  
Idrissa Sy ◽  
Aliou Diop
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Binary Data ◽  
Logistic Regression Model ◽  
Rare Event ◽  
Quantile Function ◽  
Extreme Value ◽  
Binary Response ◽  
Generalized Extreme Value ◽  
Binary Response Variable

Logistic regression model is widely used in many studies to investigate the relationship between a binary response variable Y and a set of potential predictors X. The binary response may represent, for example, the occurrence of some outcome of interest (Y=1 if the outcome occurred and Y=0 otherwise). When the dependent variable Y represents a rare event, the logistic regression model shows relevant drawbacks. In order to overcome these drawbacks we propose the Generalized Extreme Value (GEV) regression model. In particularly, we suggest the quantile function of the GEV distribution as link function. Strokes are a serious pathology and a neurological emergency involving the vital prognosis and the functional prognosis. In Senegal, strokes account for more than 30% of hospitalizations and are responsible for nearly two thirds of mortality. In this work, we use the GVE regression model for binary data to determine the risk factors leading to stroke and to develop a predictive model of life-threatening outcomes in central Sénégal.

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