Student Seminar Series - July 26, 2006
University of Minnesota
School of Statistics
College of Liberal Arts
Application
of the Monte Carlo Likelihood Approximation
Yiqun
Mou
Wednesday, July 26, 2006
10:00 AM, 127
Ford Hall
Minneapolis, East Bank Campus
Refreshments at 9:30 AM
300 Ford Hall
Abstract
Monte Carlo Markov Chain (MCMC) is a very powerful numerical method of
doing probability calculations. There are many probabilities or
expectations in statistics that can not be calculated analytically, and
MCMC is the main tool that people refer to. Eventually, MCMC will be
widely
used in all areas of statistics. Likelihood inference is an area where
MCMC
is very useful. MCMC can be used to calculate the maximum likelihood
estimator, likelihood ratio statistics, and Fisher information. Only in
simple cases, these statistics can be calculated analytically. In this
paper, I will discuss a functional approach of MCMC to the
approximation of
likelihood function, which is called Monte Carlo likelihood
approximation
(MCLA). MCLA tries to approximate the whole likelihood function with a
single sample and is very convenient to do likelihood inference, which
will
be illustrated in the examples later.