Goodness-of-fit of integrated population models using calibrated simulation

Besbeas, Panagiotis and Morgan, Byron J. T. (2014) Goodness-of-fit of integrated population models using calibrated simulation. Methods in Ecology and Evolution, 5 (12). pp. 1373-1382. ISSN 2041-210X. (doi:https://doi.org/10.1111/2041-210X.12279) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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Official URL
http://dx.doi.org/10.1111/2041-210X.12279

Abstract

1. Integrated population modelling is proving to be an important and useful technique in statistical ecology. However, there is currently no simple formal method for judging how well models fit data, when potentially sev- eral different data sets described by different structured models are being analysed in combination. 2. We propose and evaluate a new approach, of calibrated simulation. Here, comparative data sets are obtained from simulating data when model parameter values are obtained from the assumed asymptotic normal distribu- tion of the maximum-likelihood estimators from the real data. The approach is motivated and justified by Baye- sian P-values. Calibration of the resulting statistics is achieved as repeated data sets are easily simulated from the fitted model. The method requires the specification of model discrepancy measures, and we show how different measures can highlight different aspects of fit. 3. Calibration is only strictly necessary if the statistics proposed may appear to be extreme. 4. The approach of using calibrated simulation to check the goodness-of-fit of integrated population models is demonstrated by application to data sets on lapwings and herons. In each case, there are two data sets involved in the integrated analysis, and for each component data set, discrepancy measures of goodness-of-fit are obtained. For the lapwing application, as replication is efficient, it is possible to calibrate the procedure simply by using additional simulations. The heron application is shown to be feasible, but is substantially harder to cali- brate, due to the presence of productivity thresholds that need to be estimated using profile likelihood methods. We demonstrate the importance of taking more than one discrepancy measure for time-series data. Avenues for future research are outlined. This article has supplementary materials on line.

Item Type: Article
Uncontrolled keywords: asymptotic normality;discrepancy measure;goodness-of-fit;herons;integrated population modelling;kernel density estimation, lapwings
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Q Science > QH Natural history > QH541 Ecology
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Byron Morgan
Date Deposited: 09 Jun 2015 12:58 UTC
Last Modified: 12 Jun 2015 13:23 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/48964 (The current URI for this page, for reference purposes)
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