Electronic Journal of Statistics
- Electron. J. Statist.
- Volume 7 (2013), 1632-1654.
A simple approach to maximum intractable likelihood estimation
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.
Electron. J. Statist., Volume 7 (2013), 1632-1654.
First available in Project Euclid: 19 June 2013
Permanent link to this document
Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Rubio, F. J.; Johansen, Adam M. A simple approach to maximum intractable likelihood estimation. Electron. J. Statist. 7 (2013), 1632--1654. doi:10.1214/13-EJS819. https://projecteuclid.org/euclid.ejs/1371649229