Composite Likelihood Bayesian Information Criteria for Model Selection in High-Dimensional Data

Gao, Xin; Song, Peter X.‐K.

doi:10.1198/jasa.2010.tm09414

Cited by 110 publications

(95 citation statements)

References 31 publications

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“…The analysis is different from that in Gao and Song [4] in that our concern is not in whether the correct model is asymptotically chosen with probability 1. If models being compared are close to each other, then any of the models could be chosen with positive probability, and we are interested in where CLAIC and AIC might differ.…”

Section: Simulation Studiesmentioning

confidence: 90%

“…For CLBIC, it is a special case of Theorem 1 and 2 in Gao and Song [4]. A detailed treatment on the order consistency can be found in Gao and Song [4].…”

Section: A3 Proof Of Theorem 31mentioning

confidence: 99%

“…Consider the composite likelihood versions of Akaike information criterion (AIC) and Bayesian information criterion (BIC) described in Varin and Vidoni [15], Gao and Song [4], Varin et al [14]. They are defined as (with superscript for model M omitted): CLAIC = −2L CL (θ CL ) + 2tr{J(θ CL )H −1 (θ CL )} (2.3) and CLBIC = −2L CL (θ CL ) + (log n) tr{J(θ CL )H −1 (θ CL )}.…”

Section: Composite Likelihood Information Criteriamentioning

confidence: 99%

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Model comparison with composite likelihood information criteria

Ng¹,

Joe²

2014

Bernoulli

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Comparisons are made for the amount of agreement of the composite likelihood information criteria and their full likelihood counterparts when making decisions among the fits of different models, and some properties of penalty term for composite likelihood information criteria are obtained. Asymptotic theory is given for the case when a simpler model is nested within a bigger model, and the bigger model approaches the simpler model under a sequence of local alternatives. Composite likelihood can more or less frequently choose the bigger model, depending on the direction of local alternatives; in the former case, composite likelihood has more "power" to choose the bigger model. The behaviors of the information criteria are illustrated via theory and simulation examples of the Gaussian linear mixed-effects model.

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Section: Simulation Studiesmentioning

confidence: 90%

“…For CLBIC, it is a special case of Theorem 1 and 2 in Gao and Song [4]. A detailed treatment on the order consistency can be found in Gao and Song [4].…”

Section: A3 Proof Of Theorem 31mentioning

confidence: 99%

Section: Composite Likelihood Information Criteriamentioning

confidence: 99%

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Model comparison with composite likelihood information criteria

Ng¹,

Joe²

2014

Bernoulli

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“…and, based on the results of Gao & Song (2010), the PML Bayesian information criterion, BIC P L , is defined as:…”

Section: Pairwise Likelihood Model Selection Criteriamentioning

confidence: 99%

“…Pace et al (2011) present a Wald test, score test, and adjusted likelihood ratio test statistic for testing the hypothesis that a subset of parameters is equal to a specific value. Moreover, the model selection criteria AIC and the BIC are appropriately adjusted to hold under CL (Gao & Song, 2010;Varin et al, 2011;Varin & Vidoni, 2005). CL has gained attention because of its low computational complexity, which is not affected by model size.…”

Section: Introductionmentioning

confidence: 99%

Pairwise Likelihood Ratio Tests and Model Selection Criteria for Structural Equation Models with Ordinal Variables

Katsikatsou

Moustaki

2016

Psychometrika

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This document is the author's final accepted version of the journal article. There may be differences between this version and the published version. You are advised to consult the publisher's version if you wish to cite from it.Pairwise likelihood ratio tests and model selection criteria for structural equation models with ordinal variablesCorrelated multivariate ordinal data can be analysed with structural equation models. Parameter estimation has been tackled in the literature using limited-information methods including three-stage least squares and pseudo-likelihood estimation methods such as pairwise maximum likelihood estimation. In this paper, two likelihood ratio test statistics and their asymptotic distributions are derived for testing overall goodness-of-fit and nested models respectively under the estimation framework of pairwise maximum likelihood estimation. Simulation results show a satisfactory performance of type I error and power for the proposed test statistics and also suggest that the performance of the proposed test statistics is similar to that of the test statistics derived under the three-stage diagonally weighted and unweighted least squares. Furthermore, the corresponding, under the pairwise framework, model selection criteria, AIC and BIC, show satisfactory results in selecting the right model in our simulation examples. The derivation of the likelihood ratio test statistics and model selection criteria under the pairwise framework together with pairwise estimation provide a flexible framework for fitting and testing structural equation models for ordinal as well as for other types of data. The test statistics derived and the model selection criteria are used on data on 'trust in the police' selected from the 2010 European Social Survey. The proposed test statistics and the model selection criteria have been implemented in the R package lavaan 1 .

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Partially observed competing degradation processes: modeling and inference

Bordes

Mercier

Rémy

et al. 2016

Appl Stoch Models Bus & Ind

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International audienceThe aim of the present paper is the stochastic modeling and statistical inference of a component which deteriorates over time, for prediction purpose. The deterioration is due to defects which appear one by one and next independently propagate over time. The motivation comes from an application to passive components within electric power plants, where (measurable) aw indications rst initiate (one at a time) and next grow over time. The available data come from inspections at discrete times, where only the largest aw indication is measured together with the total number of indications on each component. Though detected, too small indications cannot be measured, leading to censored observations. Taking into account this partial information coming from the eld, a specic stochastic model is proposed, where the aw indications initiate according to a Poisson process and next propagate according to competing independent gamma processes. A parametric estimation procedure is developed, tested on simulated data and then applied to the industrial case. The tted model is next used to make some prediction over the future deterioration of each component and over its residual operating time until a specied critical degradation level is reached

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Composite Likelihood Bayesian Information Criteria for Model Selection in High-Dimensional Data

Cited by 110 publications

References 31 publications

Model comparison with composite likelihood information criteria

Model comparison with composite likelihood information criteria

Pairwise Likelihood Ratio Tests and Model Selection Criteria for Structural Equation Models with Ordinal Variables

Partially observed competing degradation processes: modeling and inference

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