Weight of evidence (WOE) coding of a nominal or discrete variable is widely used when preparing predictors for usage in binary logistic regression models. When using WOE coding, an important preliminary step is binning of the levels of the predictor to achieve parsimony without giving up predictive power. These concepts of WOE and binning are extended to ordinal logistic regression in the case ...

background : in medical studies, when the joint prediction about occurrence of two events should be anticipated, a statistical bivariate model is used. due to the limitations of usual statistical models, other methods such as artificial neural network (ann) and hybrid models could be used. in this paper, we propose a hybrid artificial neural network-genetic algorithm (ann-ga) model to predictio...

Models for fitting longitudinal binary responses are explored using a panel study of voting intentions. A standard multilevel repeated measures logistic model is shown to be inadequate due to the presence of a substantial proportion of respondents who maintain a constant response over time. A multivariate binary response model is shown to be a better fit to the data. SOME

We aim at proposing a Generalized Linear Model (GLM) with binary dependent variable Y , whose link function defined by the Generalized Extreme Value (GEV) distribution. We define this model as GEV regression. The goal of this paper is to overcome the drawbacks shown by the logistic regression in rare events: the probability of rare events is underestimated and the logit link is a symmetric func...

We propose a new deep architecture for topic modeling, based on Poisson Factor Analysis (PFA) modules. The model is composed of a Poisson distribution to model observed vectors of counts, as well as a deep hierarchy of hidden binary units. Rather than using logistic functions to characterize the probability that a latent binary unit is on, we employ a Bernoulli-Poisson link, which allows PFA mo...

From both the structural and functional points of view, β-turns play important biological roles in proteins. In the present study, a novel two-stage hybrid procedure has been developed to identify β-turns in proteins. Binary logistic regression was initially used for the first time to select significant sequence parameters in identification of β-turns due to a re-substitution test procedure. Se...

We present a random effects logistic approach for estimating the efficacy of treatment for compliers in a randomized trial with treatment non-adherence and longitudinal binary outcomes. We use our approach to analyse a primary care depression intervention trial. The use of a random effects model to estimate efficacy supplements intent-to-treat longitudinal analyses based on random effects logis...

Binary Logistic Regression (BLR) has been developed as non-linear models to establish quantitative structure- activity relationships (QSAR) between structural descriptors and biochemical activity of carbonic anhydrase inhibitors. Using a training set consisted of 21 compounds with known ki values, the model was trained and tested to solve two-class problems as active or inactive on the basi...

Logistic regression is a commonly used classification algorithm in machine learning. It allows categorizing data into discrete classes by learning the relationship from given set of labeled data. learns linear dataset and then introduces nonlinearity through an activation function to determine hyperplane that separates points two subclasses. In case logistic regression, most perform binary clas...

The paper presents a straightforward extension of the Bradley–Terry–Luce model (BTL model) that can be derived from the logistic threshold model of psychophysics which assumes that psychometric functions are logistic probability functions. It is shown that (under weak side conditions) the logistic threshold model is a submodel of the extended BTL model. Moreover, representation and uniqueness t...