This paper presents an approach for simultaneously estimating farmers’ decisions to accept incentive payments in return for adopting a bundle of environmentally benign management practices. Using the results of a multinomial probit analysis of surveys of over 1,000 farmers facing ten adoption decisions in an EQIP-type program, we show how the farmers’ perceptions of the desirability of various ...

In this paper we discuss parameter identification and likelihood evaluation for multinomial multiperiod Probit models. It is shown in particular that the standard autoregressive specification used in the literature can be interpreted as a latent common factor model. However, this specification is not invariant with respect to the selection of the baseline category. Hence, we propose an alternat...

This paper compares different versions of the simulated counterparts of the Wald test, the score test, and the likelihood ratio test in the multiperiod multinomial probit model. Monte Carlo experiments show that the simple form of the simulated likelihood ratio test delivers the most favorable test results in the five-period three-alternative probit model considered here. This result applies to...

We developed a multinomial ordinal probit model with singular value decomposition for testing a large number of single nucleotide polymorphisms SNPs simultaneously for association with multidisease status when sample size is much smaller than the number of SNPs. The validity and performance of the method was evaluated via simulation. We applied the method to our real study sample recruited thro...

A critical issue for the construction of genetic regulatory networks is the identification of network topology from data. In the context of deterministic and probabilistic Boolean networks, as well as their extension to multilevel quantization, this issue is related to the more general problem of expression prediction in which we want to find small subsets of genes to be used as predictors of t...

In this paper we introduce a new flexible mixed model for multinomial discrete choice where the key individualand alternative-specific parameters of interest are allowed to follow an assumptionfree nonparametric density specification while other alternative-specific coefficients are assumed to be drawn from a multivariate normal distribution which eliminates the independence of irrelevant alter...

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 ...

In this paper we introduce new robust estimators for the logistic and probit regressions for binary, multinomial, nominal and ordinal data and apply these models to estimate the parameters when outliers or inluential observations are present. Maximum likelihood estimates don't behave well when outliers or inluential observations are present. One remedy is to remove inluential observations from ...

This paper examines necessary and sufficient conditions for the existence of Maximum Likelihood Estimates (MLE) and the propriety of the posterior under a bounded improper prior density for a wide class of discrete (or multinomial) choice models. The choice models are based on the principle of utility maximization. Our results cover a wide class of latent variable distributions defining the uti...