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Model selection

Known as: Model comparison 
Model selection is the task of selecting a statistical model from a set of candidate models, given data. In the simplest cases, a pre-existing set of… 
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Related topics

Related topics

39 relations
Additive modelAkaike information criterionAlgorithmic information theoryAll models are wrong

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2011
Highly Cited
2011
The Generalized DINA Model Framework
The G-DINA (generalized deterministic inputs, noisy “and” gate) model is a generalization of the DINA model with more relaxed… 
Highly Cited
2011
Highly Cited
2011
Using the ACT-R architecture to specify 39 quantitative process models of decision making
Hypotheses about decision processes are often formulated qualitatively and remain silent about the interplay of decision… 
Highly Cited
2008
Highly Cited
2008
Comparing cost prediction models by resampling techniques
Highly Cited
2006
Highly Cited
2006
The St. Lawrence polynya and the Bering shelf circulation : New observations and a model comparison
[1] Using 14 yearlong instrumented moorings deployed south of St. Lawrence Island, along with oceanographic drifters, we… 
Highly Cited
2006
Highly Cited
2006
A Bias-Variance-Complexity Trade-Off Framework for Complex System Modeling
This study proposes a new complex system modeling approach by extending a bias-variance trade-off into a bias-variance-complexity… 
Highly Cited
2005
Highly Cited
2005
Statistical modeling and conceptualization of natural images
Highly Cited
2004
Highly Cited
2004
Estimation for the number of components in a mixture model using stepwise split-and-merge EM algorithm
Highly Cited
1997
Highly Cited
1997
Subregion-Adaptive Integration of Functions Having a Dominant Peak
Abstract Many statistical multiple integration problems involve integrands that have a dominant peak. In applying numerical… 
Highly Cited
1995
Highly Cited
1995
Bayesian Learning for Neural Networks
Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions… 
Highly Cited
1987
Highly Cited
1987
A reduced hessian strategy for sensitivity analysis of optimal flowsheets
An efficient and rigorous strategy is presented for evaluating the first-order sensitivity of the optimal solution to changes in… 
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