scholarly journals Minimum description length model selection criteria for generalized linear models

2003 ◽  
pp. 145-163 ◽  
Author(s):  
Mark H. Hansen ◽  
Bin Yu
Keyword(s):  
Model Selection ◽  
Generalized Linear Models ◽  
Selection Criteria ◽  
Linear Models ◽  
Minimum Description Length ◽  
Model Selection Criteria

ON DIFFERENT MODEL SELECTION CRITERIA IN A FORWARD AND BACKWARD REGRESSION HYBRID NETWORK

2004 ◽  
Vol 18 (05) ◽  
pp. 847-865 ◽  
Author(s):  
SHIMON COHEN ◽  
NATHAN INTRATOR
Keyword(s):  
Model Selection ◽  
Selection Criteria ◽  
Minimum Description Length ◽  
Selection Criterion ◽  
Network Size ◽  
Hybrid Network ◽  
Data Sets ◽  
Ratio Test ◽  
Selection Algorithm ◽  
Model Selection Criteria

An assessment of the performance of a hybrid network with different model selection criteria is considered. These criteria are used in an automatic model selection algorithm for constructing a hybrid network of radial and perceptron hidden units for regression. A forward step builds the full hybrid network; a model selection criterion is used for controlling the network-size and another criterion is used for choosing the appropriate hidden unit for different regions of input space. This is followed by a conservative pruning step using Likelihood Ratio Test, Bayesian or Minimum Description Length, which leads to robust estimators with low variance. The result is a small architecture that performs well on difficult, realistic, benchmark data-sets of high dimensionality and small training size. Best results are obtained by using the Bayesian approach for the model selection.

scholarly journals Spherical Minimum Description Length

Entropy ◽  
2018 ◽  
Vol 20 (8) ◽  
pp. 575
Author(s):  
Trevor Herntier ◽  
Koffi Ihou ◽  
Anthony Smith ◽  
Anand Rangarajan ◽  
Adrian Peter
Keyword(s):  
Model Selection ◽  
Parameter Space ◽  
Selection Criteria ◽  
Goodness Of Fit ◽  
Minimum Description Length ◽  
Current Model ◽  
Selection Criterion ◽  
Laplace Approximation ◽  
Model Complexity ◽  
Model Selection Criteria

We consider the problem of model selection using the Minimum Description Length (MDL) criterion for distributions with parameters on the hypersphere. Model selection algorithms aim to find a compromise between goodness of fit and model complexity. Variables often considered for complexity penalties involve number of parameters, sample size and shape of the parameter space, with the penalty term often referred to as stochastic complexity. Current model selection criteria either ignore the shape of the parameter space or incorrectly penalize the complexity of the model, largely because typical Laplace approximation techniques yield inaccurate results for curved spaces. We demonstrate how the use of a constrained Laplace approximation on the hypersphere yields a novel complexity measure that more accurately reflects the geometry of these spherical parameters spaces. We refer to this modified model selection criterion as spherical MDL. As proof of concept, spherical MDL is used for bin selection in histogram density estimation, performing favorably against other model selection criteria.

scholarly journals Generalized Measures of Divergence in Survival Analysis and Reliability

2010 ◽  
Vol 47 (1) ◽  
pp. 216-234 ◽  
Author(s):  
Filia Vonta ◽  
Alex Karagrigoriou
Keyword(s):  
Survival Analysis ◽  
Model Selection ◽  
Mutual Information ◽  
Selection Criteria ◽  
Cox Model ◽  
Transformation Model ◽  
Model Selection Criteria ◽  
Divergence Measures

Measures of divergence or discrepancy are used either to measure mutual information concerning two variables or to construct model selection criteria. In this paper we focus on divergence measures that are based on a class of measures known as Csiszár's divergence measures. In particular, we propose a measure of divergence between residual lives of two items that have both survived up to some time t as well as a measure of divergence between past lives, both based on Csiszár's class of measures. Furthermore, we derive properties of these measures and provide examples based on the Cox model and frailty or transformation model.

scholarly journals PREDICTIVE MODEL SELECTION CRITERIA FOR BAYESIAN LASSO REGRESSION

2015 ◽  
Vol 28 (1) ◽  
pp. 67-82 ◽  
Author(s):  
Shuichi Kawano ◽  
Ibuki Hoshina ◽  
Kaito Shimamura ◽  
Sadanori Konishi
Keyword(s):  
Model Selection ◽  
Predictive Model ◽  
Selection Criteria ◽  
Lasso Regression ◽  
Bayesian Lasso ◽  
Model Selection Criteria

scholarly journals Using Model Selection Criteria to Choose the Number of Principal Components

2021 ◽  
Vol 20 (3) ◽  
pp. 450-461
Author(s):  
Stanley L. Sclove
Keyword(s):  
Model Selection ◽  
Principal Components ◽  
Bayesian Information Criterion ◽  
Selection Criteria ◽  
Information Criterion ◽  
Information Criteria ◽  
Akaike's Information Criterion ◽  
Model Selection Criteria ◽  
Adequate Number ◽  
Number Of Principal Components

AbstractThe use of information criteria, especially AIC (Akaike’s information criterion) and BIC (Bayesian information criterion), for choosing an adequate number of principal components is illustrated.

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