Name
Abstract | ||
|---|---|---|
A maximum likelihood methodology for a general class of models is presented, using an approximate Bayesian computation (ABC) approach. The typical target of ABC methods is models with intractable likelihoods, and we combine an ABC-MCMC sampler with so-called \"data cloning\" for maximum likelihood estimation. Accuracy of ABC methods relies on the use of a small threshold value for comparing simulations from the model and observed data. The proposed methodology shows how to use large threshold values, while the number of data-clones is increased to ease convergence towards an approximate maximum likelihood estimate. We show how to exploit the methodology to reduce the number of iterations of a standard ABC-MCMC algorithm and therefore reduce the computational effort, while obtaining reasonable point estimates. Simulation studies show the good performance of our approach on models with intractable likelihoods such as g -and- k distributions, stochastic differential equations and state-space models. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.csda.2016.08.006 | Computational Statistics & Data Analysis |
Keywords | Field | DocType |
Approximate Bayesian computation,Intractable likelihood,MCMC,State-space model,Stochastic differential equation | Econometrics,Convergence (routing),Point estimation,Approximate Bayesian computation,Markov chain Monte Carlo,State-space representation,Threshold limit value,Stochastic differential equation,Statistics,Maximum likelihood sequence estimation,Mathematics | Journal |
Volume | Issue | ISSN |
105 | C | 0167-9473 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Umberto Picchini | 1 | 9 | 2.99 |
| AndersonRachele | 2 | 0 | 0.34 |