Composite Likelihood Bayesian Information Criteria for Model Selection in High-Dimensional Data
Journal of the American Statistical Association, 2010 For high-dimensional data sets with complicated dependency structures, the full likelihood approach often leads to intractable computational complexity. This imposes difficulty on model selection, given that most traditionally used information criteria require evaluation of the full likelihood.
Gao, Xin, Song, Peter X.-K.
openaire +3 more sources
Bayesian model choice and information criteria in sparse generalized linear models
, 2011 We consider Bayesian model selection in generalized linear models that are high-dimensional, with the number of covariates p being large relative to the sample size n, but sparse in that the number of active covariates is small compared to p. Treating the covariates as random and adopting an asymptotic scenario in which p increases with n, we show that
Foygel, Rina, Drton, Mathias
openaire +2 more sources
Examination of models for cholera: insights into model comparison methods
Letters in Biomathematics, 2016 This article provides an overview of the Akaike and Bayesian Information Criteria as applied to the setting of deterministic modelling, with the perspective that this may be a useful tool for biomathematics researchers whose primary interests lie in the ...
Olcay Akman +2 more
doaj +1 more source
A comparison of different Bayesian design criteria to compute efficient conjoint choice experiments. [PDF]
Bayesian design theory applied to nonlinear models is a promising route to cope with the problem of design dependence on the unknown parameters. The traditional Bayesian design criterion which is often used in the literature is derived from the second ...
Goos, Peter +2 more
core
A Selective Review on Information Criteria in Multiple Change Point Detection
EntropyChange points indicate significant shifts in the statistical properties in data streams at some time points. Detecting change points efficiently and effectively are essential for us to understand the underlying data-generating mechanism in modern data ...
Zhanzhongyu Gao +4 more
doaj +1 more source
Bayesian information criteria and smoothing parameter selection in radial basis function networks [PDF]
Biometrika, 2004 SUMMARY By extending Schwarz's (1978) basic idea we derive a Bayesian information criterion which enables us to evaluate models estimated by the maximum penalised likelihood method or the method of regularisation. The proposed criterion is applied to the choice of smoothing parameters and the number of basis functions in radial basis function net work ...
openaire +1 more source
On the J-test for nonnested hypotheses and Bayesian extension [PDF]
Davidson and MacKinnon’s J-test was developed to test non-nested model specification. In empirical applications, however, when the alternate specifications fit the data well the J test may fail to distinguish between the true and false models: the J test
Ghali, Moheb, Krieg, John, Rao, Surekha
core +1 more source
Model Selection Criteria in Multivariate Models with Multiple Structural Changes [PDF]
This paper considers the issue of selecting the number of regressors and the number of structural breaks in multivariate regression models in the possible presence of mul- tiple structural changes.
Eiji Kurozumi, Purevdorj Tuvaandorj
core
Structure Selection of Polynomial NARX Models using Two Dimensional (2D) Particle Swarms
, 2018 The present study applies a novel two-dimensional learning framework (2D-UPSO) based on particle swarms for structure selection of polynomial nonlinear auto-regressive with exogenous inputs (NARX) models.
Hafiz, Faizal +3 more
core +1 more source
Empirical Information Criteria for Time Series Forecasting Model Selection [PDF]
In this paper, we propose a new Empirical Information Criterion (EIC) for model selection which penalizes the likelihood of the data by a function of the number of parameters in the model.
A.B. Koehler, Md B. Billah, R.J. Hyndman
core