Simon Fraser University

Example 10.2: Kalman Filtering: Likelihood Function Evaluation

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 ...
Simon Fraser University

Example 45.3: Probit Model with Likelihood function

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 ...
Nature

Likelihood calculations to evaluate experimental designs to estimate genetic variances

Mixed model analyses via restricted maximum likelihood, fitting the so-called animal model, have become standard methodology for the estimation of genetic variances. Models involving multiple genetic ...
JSTOR Daily

Divide and Conquer: Recursive Likelihood Function Integration for Hidden Markov Models with Continuous Latent Variables

This paper develops a method to efficiently estimate hidden Markov models with continuous latent variables using maximum likelihood estimation. To evaluate the (marginal) likelihood function, I ...
JSTOR Daily

Penalized Likelihood-Ratio Test for Finite Mixture Models with Multinomial Observations

The Canadian Journal of Statistics / La Revue Canadienne de Statistique, Vol. 26, No. 4 (Dec., 1998), pp. 583-599 (17 pages) Due to the irregularity of finite mixture models, the commonly used ...