Bivariate Response Logistic Regression for Categorical Data

Categorical Data Modeling: Logistic Regression Software

A Matlab based software for logistic regression is developed to enhance the process of teaching quantitative topics and assist researchers with analyzing wide area of applications where categorical data is involved. The software offers an option of performing stepwise logistic regression to select the most significant predictors. The software includes a feature to detect influential observation...

Estimation of Count Data using Bivariate Negative Binomial Regression Models

Abstract Negative binomial regression model (NBR) is a popular approach for modeling overdispersed count data with covariates. Several parameterizations have been performed for NBR, and the two well-known models, negative binomial-1 regression model (NBR-1) and negative binomial-2 regression model (NBR-2), have been applied. Another parameterization of NBR is negative binomial-P regression mode...

Sample size determination for logistic regression

The problem of sample size estimation is important in medical applications, especially in cases of expensive measurements of immune biomarkers. This paper describes the problem of logistic regression analysis with the sample size determination algorithms, namely the methods of univariate statistics, logistics regression, cross-validation and Bayesian inference. The authors, treating the regr...

Logistic Regression for Multivariate Longitudinal Ordinal Data

Logistic Regression Classification for Uncertain Data

Logistic regression (LR) is a famous classification technique commonly used in statistics, machine learning, and data mining area of knowledge for learning a response of binary nature. It assumes that the data values are pre-determined precisely, but this is not true for all conditions. Uncertainty data arises in many applications because of data collection methodology as in repeated measures, ...