For some of my current projects, I'm probably going to need to eventually estimate some models using Metropolis-Hastings sampling. I understand the basic concepts, and the software I use (R) has ...

In this paper, parametric and empirical likelihood functions or surfaces are compared. In particular, first- and second-order expansions for log likelihood functions are developed in nonparametric and ...

Empirical likelihood methods have emerged as a robust, non‐parametric framework for statistical inference that skilfully bypasses the need for strong parametric assumptions. By constructing likelihood ...

This paper proposes three methods for computing the exact likelihood function of multivariate moving average models. Each method utilizes the structure of the covariance matrix in a different way.

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 ...

The GENMOD procedure fits a generalized linear model to the data by maximum likelihood estimation of the parameter vector .There is, in general, no closed form solution for the maximum likelihood ...