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Assessing heterogeneity in transition propensity in multi-state capture-recapture data

Jeyam, Anita, McCrea, Rachel, Pradel, Roger (2019) Assessing heterogeneity in transition propensity in multi-state capture-recapture data. Journal of the Royal Statistical Society: Series C (Applied Statistics), . ISSN 0035-9254. E-ISSN 1467-9876. (doi:10.1111/rssc.12392)

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Abstract

Multi-state capture-recapture models are a useful tool to help understand the dynamics of movement within discrete capture-recapture data. The standard multi-state capture-recapture model however relies on assumptions of homogeneity within the population with respect to survival, capture and transition probabilities. There are many ways in which this model can be generalized so that some guidance as to what is really needed is highly desirable. Within this paper we derive a new test capable of detecting heterogeneity in transition propensity and show its good power using simulation and application to a Canada goose data set. We also demonstrate that existing tests which have traditionally been used to diagnose memory are in fact sensitive to other forms of transition heterogeneity and we propose modified tests which are able to distinguish between memory and other forms of transition heterogeneity.

Item Type: Article
DOI/Identification number: 10.1111/rssc.12392
Uncontrolled keywords: Diagnostics, Goodness of fit, Markovian transitions, Statistics
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
Divisions: Faculties > Sciences > School of Mathematics Statistics and Actuarial Science > Statistics
Depositing User: Rachel McCrea
Date Deposited: 08 Nov 2019 09:31 UTC
Last Modified: 14 Jan 2020 14:47 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/78386 (The current URI for this page, for reference purposes)
McCrea, Rachel: https://orcid.org/0000-0002-3813-5328
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