Improved Monte Carlo inference for models with additive error

✍ Scribed by Martin Hazelton

• Publisher: Springer US
• Year: 1995
• Tongue: English
• Weight: 814 KB
• Volume: 5
• Category: Article
• ISSN: 0960-3174
• DOI: 10.1007/bf00162507

✦ Synopsis

Some statistical models defined in terms of a generating stochastic mechanism have intractable distribution theory, which renders parameter estimation difficult. However, a Monte Carlo estimate of the log-likelihood surface for such a model can be obtained via computation of nonparametric density estimates from simulated realizations of the model. Unfortunately, the bias inherent in density estimation can cause bias in the resulting log-likelihood estimate that alters the location of its maximizer. In this paper a methodology for radically reducing this bias is developed for models with an additive error component. An illustrative example involving a stochastic model of molecular fragmentation and measurement is given.