We derive a closed master equation for an individual-based population model in continuous space and time. The model and master equation include Brownian motion, reproduction via binary fission, and an interaction-dependent death rate moderated by a competition kernel. Using simulations we compare this individual-based model with the simplest approximation, the spatial logistic equation. In the ...

The logistic regression model is used to predict a binary response variable in terms of a set of explicative ones. The estimation of the model parameters is not too accurate and their interpretation in terms of odds ratios may be erroneous, when there is multicollinearity (high dependence) among the predictors. Other important problem is the great number of explicative variables usually needed ...

We present a scalable Bayesian model for lowrank factorization of massive tensors with binary observations. The proposed model has the following key properties: (1) in contrast to the models based on the logistic or probit likelihood, using a zero-truncated Poisson likelihood for binary data allows our model to scale up in the number of ones in the tensor, which is especially appealing for mass...

AlMCM-41 was applied for adsorption of methylene blue (MB) and auramine (AU) in single and binary component systems. In the single component systems, AlMCM-41 represents higher adsorption capacity for MB than AU with the maximal adsorption capacity of 2.07×10−4 and 1.15×10−4 mol/g at 25 ˚C for MB and AU, respectively. In the binary component system, MB and AU exhibit compe...

A method for interval estimation of scale reliability with discrete data is outlined. The approach is applicable with multi-item instruments consisting of binary measures, and is developed within the latent variable modeling methodology. The procedure is useful for evaluation of consistency of single measures and of sum scores from item sets following the two-parameter logistic model or the one...

We propose the percentile curves concept as conditional probabilities curves across representative percentiles of the distribution of curves induced by random effects in a logistic model with random intercepts. We extend this concept to a logistic model with random intercepts and slopes and propose a methodology to approximate the percentile curves using the Monte-Carlo technique. We apply this...

Recently, factorization machines (FM) have emerged as a powerful model in many applications. In this work, we study the training of FM with the logistic loss for binary classification, which is a non-linear extension of the linear model with the logistic loss (i.e., logistic regression). For the training of large-scale logistic regression, Newton methods have been shown to be an effective appro...

This chapter describes a tree-structured extension and generalization of the logistic regression method for fitting models to a binary-valued response variable. The technique overcomes a significant disadvantage of logistic regression, which is interpretability of the model in the face of multicollinearity and Simpson’s paradox. Section 1 summarizes the statistical theory underlying the logisti...

A composite interval gene mapping procedure for complex binary disease traits is proposed in this paper. The binary trait of interest is assumed to be controlled by an underlying liability that is normally distributed. The liability is treated as a typical quantitative character and thus described by the usual quantitative genetics model. Translation from the liability into a binary (disease) p...