Mixture models have been widely used in modeling of continuous observations. For the possibility to estimate the parameters of a mixture model consistently on the basis of observations from the mixture, identifiability is a necessary condition. In this study, we give some results on the identifiability of multivariate logistic mixture models.

Bartlett correction factors for likelihood ratio tests of parameters in conditional and unconditional logistic regression models are calculated. The resulting tests are compared to the Wald, likelihood ratio, and score tests, and a test proposed by Moolgavkar and Venzon in Modern Statistical Methods in Chronic Disease Epidemiology, (Wiley, New York, 1986).

Regularized Multinomial Logistic regression has emerged as one of the most common methods for performing data classification and analysis. With the advent of large-scale data it is common to find scenarios where the number of possible multinomial outcomes is large (in the order of thousands to tens of thousands) and the dimensionality is high. In such cases, the computational cost of training l...

Modelers have choices in how they approach a problem, with different approaches potentially leading to outcomes. Sometimes one gives consistently lower (or higher) result than another. The theorem and corollaries this study show that if the logistic equation or, equivalently, SI model, are perturbed at time zero by range of values mean zero, resulting trajectories must average value below (for ...

The logistic Generalized Estimating Equations (logisticGEE) models have been extensively used for analyzing clustered binary data. However, assessing the goodness-of-fit and predictability of these models is problematic due to the fact that no likelihood is available and the observations can be correlated within a cluster. In this paper we propose a new measure for estimating the generalization...

Logistic-normal topic models can effectively discover correlation structures among latent topics. However, their inference remains a challenge because of the non-conjugacy between the logistic-normal prior and multinomial topic mixing proportions. Existing algorithms either make restricting mean-field assumptions or are not scalable to large-scale applications. This paper presents a partially c...