Penalized likelihood estimation of a trivariate additive probit model

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...

Penalized Likelihood Estimation: Convergence under Incorrect Model

Penalized likelihood method is among the most eeective tools for nonparametric multivari-ate function estimation. Recently, a generic computation-oriented asymptotic theory has been developed in the density estimation setting, and been extended to other settings such as conditional density estimation, regression, and hazard rate estimation, under the assumption that the true function resides in...

Penalized Likelihood Density Estimation :

Maximum penalized likelihood estimation in a gamma-frailty model.

The shared frailty models allow for unobserved heterogeneity or for statistical dependence between observed survival data. The most commonly used estimation procedure in frailty models is the EM algorithm, but this approach yields a discrete estimator of the distribution and consequently does not allow direct estimation of the hazard function. We show how maximum penalized likelihood estimation...

Conditional Density Estimation by Penalized Likelihood Model Selection and Applications

In this technical report, we consider conditional density estimation with a maximum likelihood approach. Under weak assumptions, we obtain a theoretical bound for a KullbackLeibler type loss for a single model maximum likelihood estimate. We use a penalized model selection technique to select a best model within a collection. We give a general condition on penalty choice that leads to oracle ty...