This paper presents an easily implemented test of the assumption of a normally distributed error term for the ordered probit model. As this assumption is the central maintained hypothesis in all estimation and testing based on this model, the test ought to serve as a key specification test in applied research. A small Monte Carlo experiment suggests that the test has good size and power propert...

While a wide variety of transportation data sets involve discrete values scattered across space and time, few techniques presently exist to properly analyze such data. A new dynamic spatial ordered probit model (DSOP) is described here, and its use is demonstrated for a case of ozone concentration categories. Using outputs of photochemical models for the Austin, Texas region over a 24-hour peri...

The relationship between income and subjective well-being (SWB) is investigated using eight waves of the British Household Panel Survey and an estimation strategy that allows us to relax some assumptions typically made in the literature. First, we use a random effects generalized ordered probit model to investigate whether income effects are heterogeneous across SWB categories, and, second, we ...

Methods Based on a literature review, we identified RCTs of abatacept, adalimumab (ADA), etanercept, infliximab and tocilizumab (TCZ) in pcJIA. Comparative effectiveness was estimated on the reported American College of Rheumatology response rates (JIA ACR30/50/70/90) measured at the end of the randomised, double-blind phase by means of a Bayesian indirect comparison using a fixed-effects order...

Most genomic-enabled prediction models developed so far assume that the response variable is continuous and normally distributed. The exception is the probit model, developed for ordered categorical phenotypes. In statistical applications, because of the easy implementation of the Bayesian probit ordinal regression (BPOR) model, Bayesian logistic ordinal regression (BLOR) is implemented rarely ...

Sometimes the maximum likelihood estimation procedure for the probit model fails. There may be two reasons: the maximum likelihood estimate (MLE) just does not exist or computer overflow error occurs during the computation of the cumulative distribution function (cdf). For example, the approximation explosive effect due to an inaccurate computation of the cdf for a large value of the argument o...

In this paper, we look at conventional methods for removing endogeneity bias in regression models, including the linear model and the probit model. The usual Heckman two-step procedure should not be used in the probit model: from a theoretical perspective, this procedure is unsatisfactory, and likelihood methods are superior. However, serious numerical problems occur when standard software pack...

A mixed probit model was applied to survey data toanalyze consumers’ wilingness to buy apples with cosmetic damage caused by the sooty blotch and flyspeck (SBFS) disease complex. The analysis finds consumers will pay a premium for organic production methods and for apples with low amounts of SBFS damage. Behavioral variables such as experience in growing fruit significantly affect the willingne...

Estimation formulas for the morbidity of horses infected with equine influenza virus by linear regression, logistic regression and probit transformation were developed, using data from the outbreak at the Sha Tin Racing Track in Hong Kong in 1992. Using these formulas, we estimated the equine influenza virus morbidity rates at training centers belonging to the Japan Racing Association (JRA) in ...

UNLABELLED There are limited studies on quantifying the impact of patient satisfaction with pharmacist consultation on patient medication adherence. OBJECTIVES The objective of this study is to evaluate the effect of patient satisfaction with pharmacist consultation services on medication adherence in a large managed care organization. METHODS We analyzed data from a patient satisfaction su...