The Clark approximation, in which the maximum of two normally distributed random variables is approximated by a third normally distributed random variable, forms the basis of a relatively inexpensive ...

Nowadays a common problem when processing data sets with the large number of covariates compared to small sample sizes (fat data sets) is to estimate the parameters associated with each covariate.

and is the normal probability function. This is the likelihood function for a binary probit model. This likelihood is strictly positive so that you can take a square root of and use this as your ...

Data Generation Function: Simulates datasets suitable for Ordinal Probit Regression Model. Model Fitting + AIC-Based Variable Selection Model Fitting + BIC-Based Variable Selection Model Fitting + ...

This is a preview. Log in through your library . Abstract I consider an ordered probit model in which some of the observations in one of the categories are missing. The model can be estimated because ...