A simple approach to maximum intractable likelihood estimation

作者：Rubio F J^*; Johansen Adam M

来源：Electronic Journal of Statistics, 2013, 7(0): 1632-1654.

DOI：10.1214/13-EJS819

• Approximate Bayesian Computation
• density estimation
• maximum likelihood estimation
• Monte Carlo methods

Approximate Bayesian Computation (ABC) can be viewed as an analytic approximation of an intractable likelihood coupled with an elementary simulation step. Such a view, combined with a suitable instrumental prior distribution permits maximum-likelihood (or maximum-a-posteriori) inference to be conducted, approximately, using essentially the same techniques. An elementary approach to this problem which simply obtains a nonparametric approximation of the likelihood surface which is then maximised is developed here and the convergence of this class of algorithms is characterised theoretically. The use of non-sufficient summary statistics in this context is considered. Applying the proposed method to four problems demonstrates good performance. The proposed approach provides an alternative for approximating the maximum likelihood estimator (MLE) in complex scenarios.

• 出版日期2013