In an effort to understand how academic scientists seek information relevant to their research in today’s environment of ubiquitous electronic access, a correlation framework is built and regression analysis is applied to the survey results from 2,063 academic researchers in natural science, engineering, and medical science at five research universities in the United States. Previous work has r...

The nested logit model has been used extensively to model multi-dimensional choice situations. A drawback of the nested logit model is that it does not allow choice alternatives to share common unobserved attributes along all the dimensions characterizing the multi-dimensional choice context. This paper formulates a mixed multinomial logit structure that accommodates unobserved correlation acro...

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...

When a binary or ordinal regression model incorrectly assumes that error variances are the same for all cases, the standard errors are wrong and (unlike OLS regression) the parameter estimates are biased. Heterogeneous choice (also known as location-scale or heteroskedastic ordered) models explicitly specify the determinants of heteroskedasticity in an attempt to correct for it. Such models are...

This article proposes a method for multiclass classification problems using ensembles of multinomial logistic regression models. A multinomial logit model is used as a base classifier in ensembles from random partitions of predictors. The multinomial logit model can be applied to each mutually exclusive subset of the feature space without variable selection. By combining multiple models the pro...

1. The Effect of Response Level Ordering on Parameter Estimate Interpretation 2. Odds Ratios 2.1 Binary Explanatory Variable Modeling the Event 2.2 Binary Explanatory Variable Modeling the Nonevent 2.3 Continuous Explanatory Variable 3. Predicted Probabilities 4. Predicted by Observed Classification Tables 4.1 Classification Using Predicted Probabilities 4.2 Classification Using Bias-adjusted P...

Numerous models of travel timing have been calibrated in the literature. Some treat time as a discrete variable using familiar discrete choice methods, while others have treated time in a continuous fashion. Both approaches offer distinct advantages. Here, a continuous logit model of work tour departure time choice is estimated, which offers the advantage of continuous-time response using a ran...

the paper examines industrial concentration in iranian food products and beverages industries using firm level data aggregated to the 4-digit isic industry level between 2002 and 2004. based on different concentration indices the average level of concentration has increased slightly for the period of study. the empirical results show that increase in the level of concentration is more likely in...

Estimation of disaggregate mode choice models to estimate the ridership share on a proposed new (or improved) intercity travel service and to identify the modes from which existing intercity travelers will be diverted to the new or upgraded service constitutes a critical part of evaluating alternative travel service proposals to alleviate intercity travel congestion. This paper develops a new h...

Psychologists and sociologists usually interpret happiness scores as cardinal and comparable across respondents, and thus run OLS regressions on happiness and changes in happiness. Economists usually assume only ordinality and have mainly used ordered latent response models, thereby not taking satisfactory account of fixed individual traits. We address this problem by developing a conditional e...