Approximate maximum likelihood estimation using data-cloning ABC

Forskningsoutput: TidskriftsbidragArtikel i vetenskaplig tidskrift

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 are 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.

Detaljer

Författare
Enheter & grupper
Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Sannolikhetsteori och statistik

Nyckelord

Originalspråkengelska
Sidor (från-till)166-183
Antal sidor18
TidskriftComputational Statistics & Data Analysis
Volym105
Tidigt onlinedatum2016 aug 19
StatusPublished - 2017
PublikationskategoriForskning
Peer review utfördJa

Related projects

Umberto Picchini, Julie Forman, Kresten Lindorff-Larsen & Samuel Wiqvist

2015/01/01 → …

Projekt: ForskningInternationellt samarbete, Tvärvetenskaplig forskning

Visa alla (1)