The bivariate probit model is frequently used for estimating the effect of an endogenous binary regressor (the 'treatment') on a binary health outcome variable. This paper discusses simple modifications that maintain the probit assumption for the marginal distributions while introducing non-normal dependence using copulas. In an application of the copula bivariate probit model to the effect of ...

The goal of this study is to introduce a statistical method regarding the analysis of specific latent data for regression analysis of the discrete data and to build a relation between a probit regression model (related to the discrete response) and normal linear regression model (related to the latent data of continuous response). This method provides precise inferences on binary and multinomia...

3 Model Specification 5 3.1 Binary choice . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.1.1 The binary probit model . . . . . . . . . . . . . . . . . . 6 3.1.2 The binary logit model . . . . . . . . . . . . . . . . . . . 7 3.2 More than two choices . . . . . . . . . . . . . . . . . . . . . . . 8 3.2.1 The multinomial probit model . . . . . . . . . . . . . . . 8 3.2.2 The multinomi...

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 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...

For a binary outcome Y, generated by simple threshold crossing model with single exogenous normally distributed explanatory variable X, the OLS estimator of coefficient on X in linear probability is consistent average partial effect X. Even this very setting, we show that when allowing for to be endogenously determined, 2SLS estimator, using instrumental Z, does not identify same causal paramet...

For the analysis of caries experience in seven year old children we explored the association between the presence or absence of caries experience among different deciduous molars within each child. Some of the observed high associations have an etiological basis (e.g., between symmetrically opponent molars), while others (diagonally opponent molars) are assumed to be the result of the transitiv...

Probit models belong to the class of latent variable threshold models for analyzing binary data. They arise by assuming that the binary response is the indicator of the event that an unobserved latent variable exceeds a given threshold. Estimation can be done either in a likelihood or a Bayesian framework. The probit models can be generalized for the analysis of a variety of qualitative and lim...

Categorical outcome (or discrete outcome or qualitative response) regression models are models for a discrete dependent variable recording in which of two or more categories an outcome of interest lies. For binary data (two categories) probit and logit models or semiparametric methods are used. For multinomial data (more than two categories) that are unordered, common models are multinomial and...