An Introduction to Model Selection.

By Walter Zucchini. Journal of Mathematical Psychology, 2000

518 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/51bb241801c342dbdcf4cb212eef4634797c15b9

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Citations in the corpus

Selection of the best fit flood frequency distribution and parameter estimation procedure: a case study for Tasmania in Australia (2011, background): “Zucchini (2000) describes multiple criteria, which take into account which of the selected distributions provides the best estimation accuracy with reduced uncertainty.”
Reliable estimation of prediction errors for QSAR models under model uncertainty using double cross-validation (2014, unknown): “On average, model selection bias increases with the number of inspected models during the search [21,31].”
Information-theoretic approaches to statistical analysis in behavioural ecology: an introduction (2010, background): “Finding a support for a hypothesis that best approximates the reality calls for the statistical problem of model selection, in which the observer seeks the most appropriate model(s) for the data (McQuarrie and Tsai 1998: 1–2; Forster 2000; Zucchini 2000; Burnham and Anderson 2002: 13–14; Johnson and”
Cognitive modeling of decision making in sports (2006, methodology): “The goal of these methods is to select a preferred model based on relevant criteria such as explanatory power, parsimony, etc. (see Zucchini, 2000, for an introduction; and the remainder of Myung, Forster, and Brownse, 2000, for detailed procedures).”
Matrix factorization algorithms for the identification of muscle synergies: evaluation on simulated and experimental data sets. (2006, background): “This problem of model order selection has been addressed by many researchers and a large number of different criteria have been proposed to choose the correct number of basis vectors (Akaike 1987; Basilevsky 1994; Minka 2000; Zucchini 2000).”
Predictability: Recent insights from information theory (2007, methodology): “The question of what truncation gives the ‘‘best’’ regression model then is the problem of model selection, for which there are many methods, including in-sample criteria like the Akaike information criterion, the Bayesian information criterion, and generalized cross-validation and out-of-sample met”
Kullback-Leibler information as a basis for strong inference in ecological studies (2001, background): “An important feature of this multiplicity is that the number of alternative models should be kept small (Zucchini 2000); the analysis of, say, hundreds of models is not justified except when prediction is the only objective, or in the most exploratory phases of an investigation.”
Nonlinear Regression Models and Applications in Agricultural Research (2015, methodology): “Depending on the structure of the models, different statistical criteria can be used to find the best model: F test, Akaike information criterion (AIC), Bayesian information criterion (BIC), or the likelihood ratio test (Zucchini, 2000; Burnham and Anderson, 2002; Hoffmann, 2005; Ritz and Streibig, ”
DEALINGWITHUNCERTAINTY INAGENT-BASED SIMULATION:FARM-LEVELMODELING OFADAPTATION TOCLIMATECHANGE INSOUTHWESTGERMANY (2015, background): “Overparameterization, however, limits the extent of possible calibration of these parameters without compromising the process-based nature of the model through overfitting (Reichert and Omlin 1997; Zucchini 2000).”
Sensitivity and Specificity of Information Criteria (2018, unknown): “Many model selection techniques have been proposed in different settings (see, e.g., Miller, 2002; Pitt and Myung, 2002; Zucchini, 2000).”
Generalized feature extraction for structural pattern recognition in time-series data (2001, methodology): “Model selection [92] using a formal description length metric, such as Akaike’s I nformation Criterion (AIC) [1] or Bayesian Information Criterion (BIC) [77], could be used to find the optimal number of subregions by balancing the error with a penalty which increases with the number of subregions.”
Inferences under time pressure: how opportunity costs affect strategy selection. (2008, background): “The model with the closest fit is usually chosen as the most appropriate by taking its complexity into account ( Zucchini, 2000 ).”
Model selection and model averaging in phylogenetics: advantages of akaike information criterion and bayesian approaches over likelihood ratio tests. (2004, background): “Selection bias (Zucchini, 2000) may occur when the number of candidate models is large.”