For regression models other than the linear model, R-squared type goodness-of-fit summary statistics have been constructed for particular models using a variety of methods. We propose an R-squared measure of goodness of fit for the class of exponential family regression models, which includes logit, probit, Poisson, geometric, gamma and exponential. This R-squared is defined as the proportionat...

We address function misspecification and model heterogeneity, two critical issues in empirical work. A nonparametric approach is proposed for single-index, binary-choice models when parametric models such as Probit and Logit are potentially misspecified. The new approach involves two steps: first, we estimate index coefficients using sliced inverse regression without knowing the conditional pro...

A Monte Carlo experiment is undertaken to examine the small sample properties of three alternative estimators of a bivariate probit model with selection. The three estimators are the censored probit estimator, single-equation probit applied to the selected sub-sample and single-equation probit applied to the full sample. These estimators are compared in terms of properties of coefficient estima...

We study the problem of identifying bat species from echolocation calls in order to build automated bioacoustic monitoring algorithms. We employ the Dynamic Time Warping algorithm which has been successfully applied for bird flight calls identification and show that classification performance is superior to hand crafted call shape parameters used in previous research. This highlights that gener...

This paper ooers a model of three-party elections which allows voters to combine retrospective economic evaluations with considerations of the positions of the parties in the issue-space as well as the issue-preferences of the voters. We describe a model of British elections which allows voters to consider simultaneously all three parties, rather than limiting voters to choices among pairs of p...

The logistic normal distribution has recently been adapted via the transformation of multivariate Gaussian variables to model the topical distribution of documents in the presence of correlations among topics. In this paper, we propose a probit normal alternative approach to modelling correlated topical structures. Our use of the probit model in the context of topic discovery is novel, as many ...

This paper analyzes recent household survey data from Gansu, a less developed province in Northwest China, to examine school attainment in a poor rural area of China. Censored ordered probit regressions are used to estimate the determinants of years of schooling. Child nutritional status, as measured by height-for-age Zscores, and household income have positive effects on completed years of sch...

This paper presents a Bayesian analysis of an ordered probit model with endogenous selection. The model can be applied when analyzing ordered outcomes that depend on endogenous covariates that are discrete choice indicators modeled by a multinomial probit model. The model is illustrated by analyzing the effects of different types of medical insurance plans on the level of hospital utilization, ...

Title: WEATHER IMPACT ON ROAD ACCIDENT SEVERITY IN MARYLAND YUE LIU, M.S. in Civil and Environmental Engineering, 2013 Directed By: Professor and Chair Ali Haghani, Department of Civil and Environmental Engineering This study was conducted to analyze and quantify the impact of weather factors on road accident severity, based on Maryland accident data during 2007-2010. In order to find a better ...

There are concerns about the attachment of immigrants to the labor force, and the potential policy responses. This paper uses a bi-national survey on immigrant performance to investigate the sorting of individuals into full-time paid-employment and entrepreneurship and their economic success. Particular attention is paid to the role of legal status at entry in the host country (worker, refugee,...