In the present paper a mixed generalized estimating/pseudo-score equations (GEPSE) approach together with a distribution-free multiple imputation technique is proposed for the estimation of regression and correlation structure parameters of multivariate probit models with missing values for an ordered categorical time-invariant variable. Furthermore, a generalization of the squared trace correl...

A probit analysis method is presented for the analysis of preference data from a behavioural assay of animals, where responses were observed as animals' cheice between the treatment and control sides or areas. Standard probit models designed for alt or nothing phenomena are not applicable topreflerence assays, because a leg dose metameter only indirectly correlates with the choice. The present ...

Analysis of adjustment loans often overlooks their repetition to the same country. Repetition changes the nature of the selection problem. None of the top 20 recipients of repeated adjustment lending over 1980–99 were able to achieve reasonable growth and contain all policy distortions. About half of the adjustment loan recipients show severe macroeconomic distortions regardless of cumulative a...

Abstract: Most common regression models for analyzing binary random variables are logistic and probit regression models. However it is well known that the estimates of regression coefficients for these models are not robust to outliers [26]. The robit regression model [1, 16] is a robust alternative to the probit and logistic models. The robit model is obtained by replacing the normal (logistic...

Samples of 6, 1st generation larval populations of Boophilus decoloratus, originating from field collected females, were subjected to increasing doses of the organophosphate acaricide, Dioxathion. The dose-response relationship for 3 populations showed random heterogeneity, while systematic deviations from the linear probit lines were observed for the other 3 populations. Logistic and Gompertz ...

This paper introduces a new classifier based on the multi-class probit regressiton model for interval symbolic data. Each pattern of the learning set is described by a vector o features. Each feature is represented by an interval. Two versions of this classifier are considered. The first version fits a multi-class probit regression model conjointly on the lower and upper bounds of the interval ...

Investigating the mutual relationship between happiness and productivity using logit probit regression (Case Study: LTU)