A computationally attractive model for the analysis of conjoint choice experiments is the mixed multinomial logit model, a multinomial logit model in which it is assumed that the coefficients follow a (normal) distribution across subjects. This model offers the advantage over the standard multinomial logit model of accommodating heterogeneity in the coefficients of the choice model across subje...

Neural networks have proved in many ways and in a number of publications to be real challengers to statistical methods especially to logit analysis in predicting failures. However, most of the studies have used a rather small data set, very often close to only one hundred observations. Therefore, it has been difficult to say whether there are any significant differences between the methods test...

A Iogit vector model and a logit ideal point model are presented for external analysis of paired comparison preference judgments aggregated over a homogeneous group. The logit vector model is hierarchically nested within the logit ideal point model so that statistical tests are available to distinguish between these two models. Generalized least squares estimation procedures are developed to ac...

The random coefficients multinomial choice logit model, also known as the mixed logit, has been widely used in empirical choice analysis for the last thirty years. We prove that the distribution of random coefficients in the multinomial logit model is nonparametrically identified. Our approach requires variation in product characteristics only locally and does not rely on the special regressors...

The aim of this study was to identify factors predicting large post-void residual (PVR) (defined as ≥200 mL), an important unsolved problem, after an intravesical injection of onabotulinumtoxinA in patients with overactive bladder syndrome. The data showed that 133 of 290 patients had a large PVR after treatment. Multivariate analysis found that the baseline 3-day daytime frequency episodes and...

-This paper presents a financial distress prediction model that combines the approaches of neural network learning and logit analysis. This combination can retain the advantages and avoid the disadvantages of the two kinds of approaches in solving such a problem. The radial basis function network (RBFN) is adopted to construct the prediction model. The architecture of RBFN allows the grouping o...

the paper aimed to assess the factors affecting the willingness of tomato producer to produce organic products in alborz province in crop year 2010-11. the collected data was analysis by descriptive analysis and logit econometrics model. the results of descriptive analysis showed among 90 respondents, 42.2% willing for conversion to organic agriculture.  the results of logit model estimation in...

 Joint Optimization of Assortment Selection and Pricing under the Capacitated Multinomial Logit Choice Model with Product-Differentiated Price Sensitivities Ruxian Wang HP Laboratories HPL-2012-207 Multinomial Logit model; assortment optimization; multi-product price optimization Many firms face a problem to select an assortment of products and determine their prices to maximize the total prof...

In this research, we propose a novel approach for a multinomial logit model selection procedure: specifically, we apply association rules analysis to identifying potential interactions for multinomial logit modeling. Interaction effects are very common in reality, but conventional multinomial logit model selection methods typically ignore them. This is especially true for higher-order interacti...

The multinomial logit model (MNL) has for many years provided the fundamental platform for the analysis of discrete choice. The basic model’s several shortcomings, most notably its inherent assumption of independence from irrelevant alternatives (IIA) have motivated researchers to develop a variety of alternative formulations. The mixed logit model stands as one of the most significant of these...