Logistic regression for binary response variables

2008 ◽  
pp. 75-88
Keyword(s):  
Logistic Regression ◽  
Binary Response ◽  
Response Variables

Data classification with binary response through the Boosting algorithm and logistic regression

2017 ◽  
Vol 69 ◽  
pp. 62-73 ◽  
Author(s):  
Fortunato S. de Menezes ◽  
Gilberto R. Liska ◽  
Marcelo A. Cirillo ◽  
Mário J.F. Vivanco
Keyword(s):  
Logistic Regression ◽  
Data Classification ◽  
Binary Response ◽  
Boosting Algorithm

scholarly journals Longitudinal Joint Modelling of Ordinal and Overdispersed Count Outcomes: A Bridge Distribution for the Ordinal Random Intercept

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Payam Amini ◽  
Abbas Moghimbeigi ◽  
Farid Zayeri ◽  
Leili Tapak ◽  
Saman Maroufizadeh ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Normal Distribution ◽  
Ordinal Logistic Regression ◽  
Accurate Estimation ◽  
Joint Modelling ◽  
Random Intercept ◽  
Bridge Distribution ◽  
Response Variables ◽  
Normally Distributed ◽  
Ordinal Outcome

Associated longitudinal response variables are faced with variations caused by repeated measurements over time along with the association between the responses. To model a longitudinal ordinal outcome using generalized linear mixed models, integrating over a normally distributed random intercept in the proportional odds ordinal logistic regression does not yield a closed form. In this paper, we combined a longitudinal count and an ordinal response variable with Bridge distribution for the random intercept in the ordinal logistic regression submodel. We compared the results to that of a normal distribution. The two associated response variables are combined using correlated random intercepts. The random intercept in the count outcome submodel follows a normal distribution. The random intercept in the ordinal outcome submodel follows Bridge distribution. The estimations were carried out using a likelihood-based approach in direct and conditional joint modelling approaches. To illustrate the performance of the model, a simulation study was conducted. Based on the simulation results, assuming a Bridge distribution for the random intercept of ordinal logistic regression results in accurate estimation even if the random intercept is normally distributed. Moreover, considering the association between longitudinal count and ordinal responses resulted in estimation with lower standard error in comparison to univariate analysis. In addition to the same interpretation for the parameter in marginal and conditional estimates thanks to the assumption of a Bridge distribution for the random intercept of ordinal logistic regression, more efficient estimates were found compared to that of normal distribution.

scholarly journals Predicting Motor Insurance Claims Using Telematics Data—XGBoost versus Logistic Regression

Risks ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 70 ◽  
Author(s):  
Jessica Pesantez-Narvaez ◽  
Montserrat Guillen ◽  
Manuela Alcañiz
Keyword(s):  
Logistic Regression ◽  
Urban Areas ◽  
Traffic Accidents ◽  
Predictive Performance ◽  
Binary Response ◽  
Suitable Model ◽  
Insurance Company ◽  
Predictive Capacity ◽  
Numerous Model ◽  
Model Tuning

XGBoost is recognized as an algorithm with exceptional predictive capacity. Models for a binary response indicating the existence of accident claims versus no claims can be used to identify the determinants of traffic accidents. This study compared the relative performances of logistic regression and XGBoost approaches for predicting the existence of accident claims using telematics data. The dataset contained information from an insurance company about the individuals’ driving patterns—including total annual distance driven and percentage of total distance driven in urban areas. Our findings showed that logistic regression is a suitable model given its interpretability and good predictive capacity. XGBoost requires numerous model-tuning procedures to match the predictive performance of the logistic regression model and greater effort as regards to interpretation.

The logistic regression model with response variables subject to randomized response

2007 ◽  
Vol 51 (12) ◽  
pp. 6060-6069 ◽  
Author(s):  
Ardo van den Hout ◽  
Peter G.M. van der Heijden ◽  
Robert Gilchrist
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Randomized Response ◽  
Response Variables

scholarly journals Considering Spatial Correlations Between Binary Response Variables in Forestry: An Example Applied to Tree Harvest Modeling

2013 ◽  
Vol 59 (3) ◽  
pp. 253-260 ◽  
Author(s):  
Mathieu Fortin ◽  
Simon Delisle-Boulianne ◽  
David Pothier
Keyword(s):  
Binary Response ◽  
Spatial Correlations ◽  
Response Variables

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.

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