Logistic regression is a powerful technique for fitting models to data with a binary response variable, but the models are difficult to interpret if collinearity, nonlinearity, or interactions are present. Besides, it is hard to judge model adequacy because there are few diagnostics for choosing variable transformations and no true goodness-of-fit test. To overcome these problems, this article ...

Logistic regression is the most common method used to model binary response data. When the response is binary, it typically takes the form of 1/0, with 1 generally indicating a success and 0 a failure. However, the actual values that 1 and 0 can take vary widely, depending on the purpose of the study. For example, for a study of the odds of failure in a school setting, 1 may have the value of f...

Low yield of maize continues to affect the livelihood of smallholder farmers in the Zabzugu-Tatale area despite the introduction of a high yielding Obatanpa maize variety. The study used a cross-sectional survey design with 240 randomly sampled household heads growing maize to examine determinants of adoption of Obatampa varieties (IMVs) by farmers in the Zabzugu-Tatale area in the Northern Reg...

Clustered data arise commonly in practice and it is often of interest to estimate the mean response parameters as well as the association parameters. However, most research has been directed to address the mean response parameters with the association parameters relegated to a nuisance role. There is little work concerning both the marginal and association structures, especially in the semipara...

This project deals with the estimation of Logistic Regression parameters. We first review the binary logistic regression model and the multinomial extension, including standard MAP parameter estimation with a Gaussian prior. We then turn to the case of Bayesian Logistic Regression under this same prior. We review the cannonical approach of performing Bayesian Probit Regression through auxiliary...

This paper presents a Logistic Maturity Model. The goal is to provide companies with a system that allows both to assess their logistic processes current status and to outline an action plan for improvement, considering four key elements: Modeling Framework, Maturity Framework, Performance Framework and Improvement Systems.

In this paper we introduce new robust estimators for the logistic and probit regressions for binary, multinomial, nominal and ordinal data and apply these models to estimate the parameters when outliers or inluential observations are present. Maximum likelihood estimates don't behave well when outliers or inluential observations are present. One remedy is to remove inluential observations from ...

The theory of belief functions has been successfully used in many classification tasks. It is especially useful when combining multiple classifiers and when dealing with high uncertainty. Many classification approaches such as k-nearest neighbors, neural network or decision trees have been formulated with belief functions. In this paper, we propose an evidential calibration method that transfor...

In public health, demography and sociology, large-scale surveys often follow a hierarchical data structure as the surveys are based on multistage stratified cluster sampling. The appropriate approach to analyzing such survey data is therefore based on nested sources of variability which come from different levels of the hierarchy. When the variance of the residual errors is correlated between i...

The market basket data in the form of a binary user-item matrix or a binary item-user matrix can be modelled as a binary classification problem. The binary logistic regression approach tackles the binary classification problem, where principal components are predictor variables. If users or items are sparse in the training data, the binary classification problem can be considered as a cold-star...