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 ...

In this example, the log likelihood function of the SSM is computed using prediction error decomposition. The annual real GNP series, y t, can be decomposed as where ...

We study nonparametric maximum likelihood estimation of a log-concave probability density and its distribution and hazard function. Some general properties of these estimators are derived from two ...

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 ...