Multivariate Probit Regression using Simulated Maximum Likelihood

Multivariate probit regression using simulated maximum likelihood

We discuss the application of the GHK simulation method for maximum likelihood estimation of the multivariate probit regression model and describe and illustrate a Stata program mvprobit for this purpose.

Maximum likelihood multivariate calibration.

Two new approaches to multivariate calibration are described that, for the first time, allow information on measurement uncertainties to be included in the calibration process in a statistically meaningful way. The new methods, referred to as maximum likelihood principal components regression (MLPCR) and maximum likelihood latent root regression (MLLRR), are based on principles of maximum likel...

Speaker Adaptation using Maximum Likelihood Linear Regression

Z-Tests in multinomial probit models under simulated maximum likelihood estimation: some small sample properties

This paper analyzes small sample properties of several versions of z-tests in multinomial probit models under simulated maximum likelihood estimation. OurMonte Carlo experiments show that z-tests on utility function coefficients provide more robust results than z-tests on variance covariance parameters. As expected, both the number of observations and the number of random draws in the incorpora...

Appendix A5: Simulated maximum likelihood estimation of the trivariate probit model

Calculation of Multivariate Normal Probabilities by Simulation, with Applications to Maximum Simulated Likelihood Estimation We discuss methods for calculating multivariate normal probabilities by simulation and two new Stata programs for this purpose: -mdrawsfor deriving draws from the standard uniform density using either Halton or pseudo-random sequences, and an egen function -mvnp()for calc...