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On observation distributions for state space models of population survey data.

Knape, Jonas LU ; Jonzén, Niclas LU and Sköld, Martin LU (2011) In Journal of Animal Ecology 80. p.1269-1277
Abstract
1. State space models are starting to replace more simple time series models in analyses of temporal dynamics of populations that are not perfectly censused. By simultaneously modelling both the dynamics and the observations, consistent estimates of population dynamical parameters may be obtained. For many data sets, the distribution of observation errors is unknown and error models typically chosen in an ad-hoc manner. 2. To investigate the influence of the choice of observation error on inferences, we analyse the dynamics of a replicated time series of red kangaroo surveys using a state space model with linear state dynamics. Surveys were performed through aerial counts and Poisson, overdispersed Poisson, normal and log-normal... (More)
1. State space models are starting to replace more simple time series models in analyses of temporal dynamics of populations that are not perfectly censused. By simultaneously modelling both the dynamics and the observations, consistent estimates of population dynamical parameters may be obtained. For many data sets, the distribution of observation errors is unknown and error models typically chosen in an ad-hoc manner. 2. To investigate the influence of the choice of observation error on inferences, we analyse the dynamics of a replicated time series of red kangaroo surveys using a state space model with linear state dynamics. Surveys were performed through aerial counts and Poisson, overdispersed Poisson, normal and log-normal distributions may all be adequate for modelling observation errors for the data. We fit each of these to the data and compare them using AIC. 3. The state space models were fitted with maximum likelihood methods using a recent importance sampling technique that relies on the Kalman filter. The method relaxes the assumption of Gaussian observation errors required by the basic Kalman filter. Matlab code for fitting linear state space models with Poisson observations is provided. 4. The ability of AIC to identify the correct observation model was investigated in a small simulation study. For the parameter values used in the study, without replicated observations, the correct observation distribution could sometimes be identified but model selection was prone to misclassification. On the other hand, when observations were replicated, the correct distribution could typically be identified. 5. Our results illustrate that inferences may differ markedly depending on the observation distributions used, suggesting that choosing an adequate observation model can be critical. Model selection and simulations show that for the models and parameter values in this study, a suitable observation model can typically be identified if observations are replicated. Model selection and replication of observations, therefore, provide a potential solution when the observation distribution is unknown. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
maximum likelihood, model selection, observation errors, population dynamics, process errors, replication, state space models
in
Journal of Animal Ecology
volume
80
pages
1269 - 1277
publisher
Federation of European Neuroscience Societies and Blackwell Publishing Ltd
external identifiers
  • wos:000296452700017
  • pmid:21635251
  • scopus:80053653797
ISSN
1365-2656
DOI
10.1111/j.1365-2656.2011.01868.x
project
BECC
language
English
LU publication?
yes
id
e72819d5-5cc8-4387-b646-4049f13d49e3 (old id 2008579)
date added to LUP
2011-08-19 13:26:47
date last changed
2017-06-18 03:07:17
@article{e72819d5-5cc8-4387-b646-4049f13d49e3,
  abstract     = {1. State space models are starting to replace more simple time series models in analyses of temporal dynamics of populations that are not perfectly censused. By simultaneously modelling both the dynamics and the observations, consistent estimates of population dynamical parameters may be obtained. For many data sets, the distribution of observation errors is unknown and error models typically chosen in an ad-hoc manner. 2. To investigate the influence of the choice of observation error on inferences, we analyse the dynamics of a replicated time series of red kangaroo surveys using a state space model with linear state dynamics. Surveys were performed through aerial counts and Poisson, overdispersed Poisson, normal and log-normal distributions may all be adequate for modelling observation errors for the data. We fit each of these to the data and compare them using AIC. 3. The state space models were fitted with maximum likelihood methods using a recent importance sampling technique that relies on the Kalman filter. The method relaxes the assumption of Gaussian observation errors required by the basic Kalman filter. Matlab code for fitting linear state space models with Poisson observations is provided. 4. The ability of AIC to identify the correct observation model was investigated in a small simulation study. For the parameter values used in the study, without replicated observations, the correct observation distribution could sometimes be identified but model selection was prone to misclassification. On the other hand, when observations were replicated, the correct distribution could typically be identified. 5. Our results illustrate that inferences may differ markedly depending on the observation distributions used, suggesting that choosing an adequate observation model can be critical. Model selection and simulations show that for the models and parameter values in this study, a suitable observation model can typically be identified if observations are replicated. Model selection and replication of observations, therefore, provide a potential solution when the observation distribution is unknown.},
  author       = {Knape, Jonas and Jonzén, Niclas and Sköld, Martin},
  issn         = {1365-2656},
  keyword      = {maximum likelihood,model selection,observation errors,population dynamics,process errors,replication,state space models},
  language     = {eng},
  pages        = {1269--1277},
  publisher    = {Federation of European Neuroscience Societies and Blackwell Publishing Ltd},
  series       = {Journal of Animal Ecology},
  title        = {On observation distributions for state space models of population survey data.},
  url          = {http://dx.doi.org/10.1111/j.1365-2656.2011.01868.x},
  volume       = {80},
  year         = {2011},
}