Format

Send to:

Choose Destination
See comment in PubMed Commons below
Ecol Lett. 2007 Jul;10(7):551-63.

Data cloning: easy maximum likelihood estimation for complex ecological models using Bayesian Markov chain Monte Carlo methods.

Author information

  • 1Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G2G1, Canada.

Erratum in

  • Ecol Lett. 2007 Sep;10(9):866.

Abstract

We introduce a new statistical computing method, called data cloning, to calculate maximum likelihood estimates and their standard errors for complex ecological models. Although the method uses the Bayesian framework and exploits the computational simplicity of the Markov chain Monte Carlo (MCMC) algorithms, it provides valid frequentist inferences such as the maximum likelihood estimates and their standard errors. The inferences are completely invariant to the choice of the prior distributions and therefore avoid the inherent subjectivity of the Bayesian approach. The data cloning method is easily implemented using standard MCMC software. Data cloning is particularly useful for analysing ecological situations in which hierarchical statistical models, such as state-space models and mixed effects models, are appropriate. We illustrate the method by fitting two nonlinear population dynamics models to data in the presence of process and observation noise.

PMID:
17542934
[PubMed - indexed for MEDLINE]
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Blackwell Publishing
    Loading ...
    Write to the Help Desk