Model Based Inference in the Life Sciences: A Primer on Evidence

By P. Bednekoff. 2009

758 citations recorded by the source corpus.

Subfields: Information Systems and Management, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/375531d9398340cfead4ff6e7e6ccc0f0f492047

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Citations in the corpus

of construction probability on word durations during spontaneous incremental sentence production (2012, methodology): “The motivation and mathematical foundations of this approach, as well as the criticism of the methods of single best model selection are exhaustively presented in Burnham and Anderson (2002, 2004) and Lukacs et al. (2010). In the Appendix, we outline in detail the multimodel inference procedure (Bur”
Microclimatic buffering and resource‐based habitat in a glacial relict butterfly: significance for conservation under climate change (2009, methodology): “Finally, logistic regressions were used to relate caterpillar presence to vegetation characteristics using AIC-based model selection (Anderson, 2008).”
Ecological and social factors affect the occurrence and outcomes of intergroup encounters in chimpanzees (2012, positive): “Indicates parameter included in model; K: number of estimable parameters in model (1⁄4 number of covariates plus intercept); AIC: Akaike’s Information Criteria; DAIC: AICi min AIC; min AIC: minimum AIC value of all models:wi Akaike weight: weight of evidence that model i is the best model in the can”
Moonlight and shelter cause differential seed selection and removal by rodents (2011, methodology): “By using a model-averaging approach with all possible models (Anderson 2008), we established the importance of each variable.”
Gains in native species promote biotic homogenization over four decades in a human‐dominated landscape (2013, background): “…considered a trait to be related to plant indicator status when the difference in AIC (AIC intercept only minus AICincluding trait) was greater than 5, which corresponds to strength of evidence at least 12 times stronger than the alternative model (Anderson 2008; Burnham, Anderson & Huyvaert 2011).”
Establishment success of invasive ring‐necked and monk parakeets in Europe (2009, background): “Important variables are characterized by a high AICc weight and model-averaged estimates that are higher than their standard errors (Anderson, 2008).”
Model averaging, missing data and multiple imputation: a case study for behavioural ecology (2010, methodology): “The first kind of model-averaged estimate bj where the parameter βj is averaged over all models in which the predictor xj appears is given by: bj ¼ PR i¼1 wiIjðgiÞbj;i PR i¼1 wiIjðgiÞ ; ð2Þ where Ij(gi)=1 if the predictor and 0 otherwise, βj,i is the regression coefficient estimate of the predictor ”
Strengthening statistical usage in marine ecology (2012, methodology): “The information-theoretic approach to data treatment (Anderson, 2008; Burnham and Anderson, 2002) is an integrated process of a priori specification of a set of candidate models (based on the science of the problem), model selection based on the principle of parsimony, and the estimation of…”
The consequences of checking for zero‐inflation and overdispersion in the analysis of count data (2019, background): “Anderson (2007) notes that: ‘Model selection bias is subtle but its effects are widespread and little understood by many people working in the life sciences’.”
Information-theoretic approaches to statistical analysis in behavioural ecology: an introduction (2010, background): “The number of models in the candidate set should be substantially lower than sample size (Burnham and Anderson 2002: 15–19), but this condition Behav Ecol Sociobiol (2011) 65:1–11 5”
Broad-scale effects of hypoxia on benthic community structure in Chesapeake Bay, USA (2009, methodology): “We established multiple hypotheses, represented by generalized linear models and used each model's residual sum of squares with the AIC corrected for small sample size (AICc) to select the model(s) that optimized goodness-of-fit and parsimony (Burnham and Anderson, 2002; Anderson, 2008).”
A systematic review on the behavioural responses of wild marine mammals to noise: the disparity between science and policy (2016, methodology): “A pretending variable is Page 19 of 84 https://mc06.manuscriptcentral.com/cjz-pubs Canadian Journal of Zoology Draft 20 identified when the fit of the model does not improve with the added parameter (difference in log likelihoods ~ 0)(Anderson 2008).”
Poleward shifts in breeding bird distributions in New York State (2009, methodology): “Model probabilities are analogous to Akaike weights (wi) and measures the weight of evidence in favor of the model (Anderson, 2008).”
Psittacine reintroductions: Common denominators of success (2012, result): “Model weights (wi) can be interpreted as the likelihood that model i is the best model in the a priori set, and evidence ratios (wi/wj) can be interpreted as the relative support of the data for model i compared with model j (Anderson, 2008).”
Barcoding's next top model: an evaluation of nucleotide substitution models for specimen identification (2012, background): “These weights approximate probabilities for a given set of models, and evidence ratios between these weights offer a comparison of support for competing models (Anderson 2008).”
AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons (2011, methodology): “Formally, K-L information can be expressed as a difference between two statistical expectations (Burnham and Anderson 2002, p. 58 and Anderson 2008, p. 52–7).”