Different measures of the proportion of variation in a dependent variable explained by covariates are reported by different standard programs for logistic regression. We review twelve measures that have been suggested or might be useful to measure explained variation in logistic regression models. The definitions and properties of these measures are discussed and their performance is compared i...

A method for the simulation of samples from the exact posterior distributions of the parameters in logistic regression is proposed. It is based on the principle of data augmentation and a latent variable is introduced, similar to the approach of Albert and Chib (J. Am. Stat. Assoc. 88 (1993) 669), who applied it to the probit model. In general, the full conditional distributions are intractable...

Background: Regarding the increased risk of developing type 2 diabetes in pre-diabetic people, identifying pre-diabetes and determining of its risk factors seems so necessary. In this study, it is aimed to compare ordinary logistic regression and robust logistic regression models in modeling pre-diabetes risk factors. Methods: This is a cross-sectional study and conducted on 6460 people, over ...

L1 regularized logistic regression is now a workhorse of machine learning: it is widely used for many classification problems, particularly ones with many features. L1 regularized logistic regression requires solving a convex optimization problem. However, standard algorithms for solving convex optimization problems do not scale well enough to handle the large datasets encountered in many pract...

In this paper we deal with graph classification. We propose a new algorithm for performing sparse logistic regression on graphs. Sparsity is required for the reason of interpretability, which is often necessary in domains such as bioinformatics or chemoinformatics. Our method is comparable in accuracy with other methods of graph classification and produces probabilistic output in addition.

Binary logistic regression with a sparsity constraint on the solution plays a vital role in many high dimensional machine learning applications. In some cases, the features can be grouped together, so that entire subsets of features can be selected or zeroed out. In many applications, however, this can be very restrictive. In this paper, we are interested in a less restrictive form of structure...

Binomial data with unknown sizes often appear in biological and medical sciences. The previous methods either use the Poisson approximation or the quasi-likelihood approach. A full likelihood approach is proposed by treating unknown sizes as latent variables. This approach simplifies analysis as maximum likelihood estimation can be applied. It also facilitates us to gain a lot more insights int...

In this paper we present a simple hierarchical Bayesian treatment of the sparse kernel logistic regression (KLR) model based MacKay’s evidence approximation. The model is re-parameterised such that an isotropic Gaussian prior over parameters in the kernel induced feature space is replaced by an isotropic Gaussian prior over the transformed parameters, facilitating a Bayesian analysis using stan...

This paper deals with the multiple annotation problem in medical application of cancer detection in digital images. The main assumption is that though images are labeled by many experts, the number of images read by the same expert is not large. Thus differing with the existing work on modeling each expert and ground truth simultaneously, the multi annotation information is used in a soft manne...