A random-effects probit model is developed for the case in which the outcome of interest is a series of correlated binary responses. These responses can be obtained as the product of a longitudinal response process where an individual is repeatedly classified on a binary outcome variable (e.g., sick or well on occasion t), or in "multilevel" or "clustered" problems in which individuals within g...

Many financial organizations such as banks and retailers use computational credit risk analysis (CRA) tools heavily due to recent financial crises and more strict regulations. This strategy enables them to manage their financial and operational risks within the pool of financial institutes. Machine learning algorithms especially binary classifiers are very popular for that purpose. In real-life...

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...

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 ...

The Agincourt Health and Demographic Surveillance System has since 2001 conducted a biannual household asset survey in order to quantify household socio-economic status (SES) in a rural population living in northeast South Africa. The survey contains binary, ordinal and nominal items. In the absence of income or expenditure data, the SES landscape in the study population is explored and describ...

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...

Crawfish processors’ ex ante adoption rates of three hypothetical crawfish peeling machines are assessed using a polychotomous-choice elicitation format. Adoption rates would likely range from 23% to 70%, depending upon which machine was offered and whether it was purchased or leased. Processors most likely to adopt are determined using ordered probit analysis. Likely adopters would be larger, ...