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Model Selection

Given a set of candidate models, the goal of Model Selection is to select the model that best approximates the observed data and captures its underlying regularities. Model Selection criteria are defined such that they strike a balance between the goodness of fit, and the generalizability or complexity of the models.

Source: Kernel-based Information Criterion

Papers

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Showing 1921–1930 of 2050 papers

TitleStatusHype
On model misspecification and KL separation for Gaussian graphical models—0
Marginal likelihood and model selection for Gaussian latent tree and forest models—0
Model Selection in High-Dimensional Misspecified Models—0
Testing and Confidence Intervals for High Dimensional Proportional Hazards Model—0
The application of the Bayes Ying Yang harmony based GMMs in on-line signature verification—0
Quantile universal threshold: model selection at the detection edge for high-dimensional linear regression—0
How Many Communities Are There?—0
QUIC & DIRTY: A Quadratic Approximation Approach for Dirty Statistical Models—0
A Nonparametric Bayesian Approach to Uncovering Rat Hippocampal Population Codes During Spatial Navigation—0
Convex Techniques for Model Selection—0
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