Multinomial logit models which are most commonly used for the modeling of unordered multi-category responses are typically restricted to the use of few predictors. In the high-dimensional case maximum likelihood estimates frequently do not exist. In this paper we are developing a boosting technique called multinomBoost that performs variable selection and fits the multinomial logit model also w...

A major drawback associated with the use of classical statistical methods for business failure prediction on top of financial distress is their lack of high accuracy rate. This work analyses the use of the two-stage ensemble of multivariate discriminant analysis (MDA) and logit to improve predictive performance of classical statistical methods. All possible ratios are firstly built from the qua...

Logit kernel is a discrete choice model that has both probit-like disturbances as well as an additive i.i.d. extreme value (or Gumbel) disturbance à la multinomial logit. The result is an intuitive, practical, and powerful model that combines the flexibility of probit with the tractability of logit. For this reason, logit kernel has been deemed the “model of the future” and is becoming extremel...

Recently, the Markov chain choice model has been introduced by Blanchet et al. to overcome the computational intractability for learning and revenue management for several modern choice models, including the mixed multinomial logit models. However, the known methods for learning the Markov models require almost all items to be offered in the learning stage, which is impractical. To address this...

While there is growing application of generalized ordered outcome model variants (widely known as Generalized Ordered Logit (GOL) model and Partial Proportional Odds Logit (PPO) model) in crash injury severity analysis, there are several aspects of these approaches that are not well documented in extant safety literature. The current research note presents the relationship between these two var...

The main reason for carrying out this study was to determine possible relationships among several adoption parameters of computer use, internet usage and internet access in agriculture. The key options for determining relationships (apart from non-parametric correlation techniques) are canonical correlation analysis, probit models and logit models. Canonical correlation analysis is generally se...

Two-part random effects models have been used to fit semi-continuous longitudinal data where the response variable has a point mass at 0 and a continuous right-skewed distribution for positive values. We review methods proposed in the literature for analyzing data with excess zeros. A two-part logit-lognormal random effects model, a two-part logit-truncated normal random effects model, a two-pa...

Sizes of datasets used in IS research are growing quickly due to data available from digital technologies such as mobile, RFID, sensors, online markets, and more. It is not uncommon to see studies using tens and hundreds of thousands or even millions of records. Linear regression is among the most popular statistical model in social sciences research. Linear probability models, which are linear...

In this study, NRTL and UNIQUAC thermodynamic models were used to predict the composition of ternary mixtures of solvents+ m/o/p-cresol+ water in organic and aqueous phases. Various solvents are used for the separation of cresols from water. In this study, methyl propyl ketone, methyl isopropyl ketone, methyl butyl ketone, and methyl isobutyl ketone solvents were investigated. Intermolecular in...