POPULATION TIME SERIES: PROCESS VARIABILITY, OBSERVATION ERRORS, MISSING VALUES, LAGS, AND HIDDEN STATES

By James S. Clark, OTTAR N. Bjørnstad. 2004

361 citations recorded by the source corpus.

Subfields: Ecology, Modeling and Simulation, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e1a1ac265797e2b8cba8786ce16c428ba5b3376e

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Citations in the corpus

ESTIMATING DENSITY DEPENDENCE, PROCESS NOISE, AND OBSERVATION ERROR (2006, background): “‘‘State-space’’ models for analyzing time series of population abundances offer possibilities of jointly estimating the amount of observation error along with the amount of process noise (De Valpine 2002, De Valpine and Hastings 2002, Clark and Bjørnstad 2004).”
State-space models of individual animal movement. (2008, background): “SSM methods originated in engineering, but have been applied in a range of disciplines, including population ecology, to estimate population trajectories from imprecise or incomplete survey data [46, 47 ].”
A scalable end-to-end Gaussian process adapter for irregularly sampled time series classification (2016, background): “Such time series data are studied in various areas including climate science [22], ecology [4], biology [18], medicine [15] and astronomy [21].”
Resolving issues of imprecise and habitat-biased locations in ecological analyses using GPS telemetry data (2010, background): “Depending on the choice of PDOP cutoff and the magnitude of location imprecision, the proportion of data removed by screening could be as large as 80 per cent (D’Eon & Delparte 2005; Lewis et al. 2007). Lewis et al. (2007) recommend screening only two-dimensional locations, and by removing two-dimen”
Plug-and-play inference for disease dynamics: measles in large and small populations as a case study (2009, background): “2002; Clark & Bjørnstad 2004; Koelle & Pascual 2004) .”
Accounting for uncertainty in ecological analysis: the strengths and limitations of hierarchical statistical modeling. (2009, methodology): “…et al. 2006), inference on hidden states (Dupuis 1995, Ver Hoef and Cressie 1997, de Valpine 2003, Dorazio and Royle 2003, Stenseth et al. 2003, Clark and Bjornstad 2004, Gelfand et al. 2004, Maunder 2004, Clark et al. 2005, Thomas et al. 2005), quantifying the importance of interactions…”
Why environmental scientists are becoming Bayesians (2004, methodology): “We might use the model to draw inference on population sizes at many more dates than those for which we have observations (Clark & Bjornstad 2004).”
Seven Shortfalls that Beset Large-Scale Knowledge of Biodiversity (2015, background): “However, uncertainties in estimates of population size can affect the results (and conclusions) of analyses of the data in the GPDD and in most population time series (Clark & Bjørnstad 2004, Knape & de Valpine 2012).”
Understanding movement data and movement processes: current and emerging directions. (2008, methodology): “This framework, though seemingly simple, accommodates a great deal of structure in both the data and the process (Clark & Bjørnstad 2004; Jonsen et al. 2005; Patterson et al. 2008).”
Multi-Time Attention Networks for Irregularly Sampled Time Series (2020, background): “Such data occur in electronic health records (Marlin et al., 2012; Yadav et al., 2018), climate science (Schulz & Stattegger, 1997), ecology (Clark & Bjørnstad, 2004), biology (Ruf, 1999), and astronomy (Scargle, 1982).”