This paper sheds light on the causal relationship between education and health outcomes. We combine three surveys (SHARE, HRS and ELSA) that include nationally representative samples of people aged 50 and over from thirteen OECD countries. We use variation in the timing of educational reforms across these countries as an instrument for education. Using IV-Probit models, we find causal evidence ...

probabilistic approach of traffic assignment has been primarily developed to provide a more realistic and flexible theoretical framework to represent traveler’s route choice behavior in a transportation network. the problem of path overlapping in network modelling has been one of the main issues to be tackled. due to its flexible covariance structure, probit model can adequately address the pro...

BACKGROUND Structural equation models (SEMs) provide a general framework for analyzing mediated longitudinal data. However when interest is in the total effect (i.e. direct plus indirect) of a predictor on the binary outcome, alternative statistical techniques such as non-linear mixed models (NLMM) may be preferable, particularly if specific causal pathways are not hypothesized or specialized S...

In this paper we have combined fundamental analysis and contingent claim analysis into a hybrid model of credit risk measurement. We have extended the standard Merton approach to estimate a new risk neutral distance to default metric, assuming a more complex capital structure, adjusting for dividend payments, introducing randomness to the default point and allowing a fractional recovery when de...

The ordered probit regression model is used to investigate domestic real estate brokers in Taiwan, in order to determine the factors affecting the listings of realtors and provide a reference for the domestic real estate brokerage market. Based on empirical analysis, this study found that the Wald Chi-Square of six independent variables reached a significance level of 5%, while the other two in...

In this paper it is shown that using the maximum likelihood ML prin ciple for the estimation of multivariate probit models leads to consistent and normally distributed pseudo maximum likelihood regression parame ter estimators PML estimators even if the true correlation structure of the responses is misspeci ed As a consequence e g the PML estimator of the random e ects probit model may be used...

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 ...

We conducted symmetric multitask learning experiments on the landmine datasets (Xue et al., 2007). There are total of 19 tasks in the homogeneous space. We examine the performance of three methods on accuracy of label prediction: (i) cross-domain multitask learning using latent probit models, (ii) single task learning plus separate probit classifiers, (iii) simply pooling the data in all tasks ...

A broad class of models that routinely appear in several fields can be expressed as partially or fully discretized Gaussian linear regressions. Besides including classical response settings, this also encompasses probit, multinomial probit and tobit regression, among others, thereby yielding one the most widely-implemented families routine applications. The relevance such representations has st...