ESTIMATING DENSITY DEPENDENCE, PROCESS NOISE, AND OBSERVATION ERROR

By Brian Dennis, José Miguel Ponciano, Subhash R. Lele, Mark L. Taper, David F. Staples. 2006

437 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/47a24f4a86ad8d4ef018fae7eab5744ea00cc1e0

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Citations in the corpus

Dealing with collinearity in behavioural and ecological data: model averaging and the problems of measurement error (2010, positive): “The need to quantify measurement error has been emphasised in the ecological literature in the context of population modelling (Shenk et al. 1998 ;E llner et al. 2002; Dennis et al. 2006; Freckleton et al. 2006; Linden and Knape 2009) and it is increasingly appreciated that this is an important co”
Trend estimation in populations with imperfect detection (2009, methodology): “Soldaat et al. 2007 for an application) and formally partition the observed data into a stochastic state and a stochastic observation component, and may thus access features of the state, such as density-dependence, free from effects of the observation process (Lande et al. 2003; de Valpine 2003; D”
Investigating population dynamics in ungulates: Do hunting statistics make up a good index of population abundance? (2010, background): “Since these indices are prone to observation errors that enhance noise, it may be difficult to make inferences about patterns of population changewith short data sets (Solow & Steele 1990, Dennis et al. 2006).”
Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier-Stokes simulations: A data-driven, physics-informed Bayesian approach (2015, methodology): “thm of the inversion scheme is presented in Appendix B, and further details can be found in [34]. The noises added to the observations represent a combination of measurement errors and process errors [36]. The former is likely to be negligible for DNS data. However, the latter can be signicant and i”
Greater Sage-Grouse Population Dynamics and Probability of Persistence (2015, methodology): “In an earlier analysis of lek data through 2003 (Connelly et al. 2004), we tested an additional model (the Gompertz state space model [GSS] Dennis et al. 2006) incorporating observation error into the Gompertz-type density-dependent model, which consistently indicated that observation error in our…”
Effects of climate change on European ducks: what do we know and what do we need to know? (2013, background): “A step towards demographic modelling could be applying long-term duck monitoring estimates in state-space modelling of population and observation processes in parallel, thus trying to quantify observation errors and actual population regulating parameters (Dennis et al. 2006).”
The theta‐logistic is unreliable for modelling most census data (2010, neutral): “Including measurement error (sometimes also called observation error) into the model fitting is not necessarily straightforward (Dennis et al. 2006); nonetheless, as a matter of principle and practice census data should have associated estimates of measurement error; much as derived parameters must…”