Journal article
Analyzing short time series data from periodically fluctuating rodent populations by threshold models: A nearest block bootstrap approach
Science in China. Series A, Mathematics, physics, astronomy, Vol.52(6), pp.1085-1106
06/01/2009
DOI: 10.1007/s11425-009-0061-3
Abstract
The study of the rodent fluctuations of the North was initiated in its modern form with Elton's pioneering work. Many scientific studies have been designed to collect yearly rodent abundance data, but the resulting time series are generally subject to at least two "problems": being short and non-linear. We explore the use of the continuous threshold autoregressive (TAR) models for analyzing such data. In the simplest case, the continuous TAR models are additive autoregressive models, being piecewise linear in one lag, and linear in all other lags. The location of the slope change is called the threshold parameter. The continuous TAR models for rodent abundance data can be derived from a general prey-predator model under some simplifying assumptions. The lag in which the threshold is located sheds important insights on the structure of the prey-predator system. We propose to assess the uncertainty on the location of the threshold via a new bootstrap called the nearest block bootstrap (NBB) which combines the methods of moving block bootstrap and the nearest neighbor bootstrap. The NBB assumes an underlying finite-order time-homogeneous Markov process. Essentially, the NBB bootstraps blocks of random block sizes, with each block being drawn from a non-parametric estimate of the future distribution given the realized past bootstrap series. We illustrate the methods by simulations and on a particular rodent abundance time series from Kilpisjarvi, Northern Finland.
Details
- Title: Subtitle
- Analyzing short time series data from periodically fluctuating rodent populations by threshold models: A nearest block bootstrap approach
- Creators
- Kung-Sik Chan - University of IowaTong Howell - London Sch Econ, Dept Stat, Hong Kong, Hong Kong, Peoples R ChinaNils Chr Stenseth - University of Oslo
- Resource Type
- Journal article
- Publication Details
- Science in China. Series A, Mathematics, physics, astronomy, Vol.52(6), pp.1085-1106
- Publisher
- SCIENCE PRESS
- DOI
- 10.1007/s11425-009-0061-3
- ISSN
- 1006-9283
- eISSN
- 1862-2763
- Number of pages
- 22
- Grant note
- CMG-0620789 / US National Science Foundation; National Science Foundation (NSF) EP/C549058/1 / Engineering and Physical Sciences Research Council of UK; UK Research & Innovation (UKRI); Engineering & Physical Sciences Research Council (EPSRC) HKU7036/068 / Research Grants Council of Hong Kong; Hong Kong Research Grants Council
- Language
- English
- Date published
- 06/01/2009
- Academic Unit
- Statistics and Actuarial Science; Radiology
- Record Identifier
- 9984257618302771
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