A note on composite likelihood inference and model selection

Varin, Cristiano; Vidoni, Paolo

doi:10.1093/biomet/92.3.519

Cited by 287 publications

(248 citation statements)

References 35 publications

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“…Based on the results of Varin & Vidoni (2005), the Akaike PML information criterion, AIC 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%

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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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“…Based on the results of Varin & Vidoni (2005), the Akaike PML information criterion, AIC P L , is defined as:…”

Section: Pairwise Likelihood Model Selection Criteriamentioning

confidence: 99%

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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“…Some clear grounds for not selecting linear regressions as descriptors of complexity in datasets is defined within the same analysis (R 2 value), which at low values implies a low explanatory power of the observed variance in the dependent variable by the dependent (Bevington et al 1993, Varin & Vidoni 2005. Another reason why linear regression cannot fits the data significantly corresponds to the assumption that the independent variable has been properly defined and that the experimental error and biological variability affects only the values of the dependent variable, which in a pragmatic sense never occurs (Bates & Watts 1988).…”

Section: Methodsmentioning

confidence: 99%

Information criteria on multimodel selection of parametric regression: Biological applications

Lopez¹,

Román‐Palacios²

2015

RevComEst

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“…Further, the results indicated that for all variable specifications, the best spatial weight matrix specification was consistently the inverse of the continuous distance specification with the 0.25 mile distance band. This determination was based on the composite likelihood information criterion (CLIC) statistic, which may be used to compare the data fit of non-nested formulations (see Varin and Vidoni, 2005). This CLIC statistic takes the form shown below:…”

Section: Model Selection and Variable Specificationmentioning

confidence: 99%

A spatial panel ordered-response model with application to the analysis of urban land-use development intensity patterns

Ferdous¹,

Bhat

2012

J Geogr Syst

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This paper proposes and estimates a spatial panel ordered-response probit model with temporalautoregressive error terms to analyze changes in urban land development intensity levels over time. Such a model structure maintains a close linkage between the land owner's decision (unobserved to the analyst) and the land development intensity level (observed by the analyst), and accommodates spatial interactions between land owners that lead to spatial spillover effects.In addition, the model structure incorporates spatial heterogeneity as well as spatial heteroscedasticity. The resulting model is estimated using a composite marginal likelihood (CML) approach that does not require any simulation machinery and that can be applied to data sets of any size. A simulation exercise indicates that the CML approach recovers the model parameters very well, even in the presence of high spatial and temporal dependence. In addition, the simulation results demonstrate that ignoring spatial dependency and spatial heterogeneity when both are actually present will lead to bias in parameter estimation. A demonstration exercise applies the proposed model to examine urban land development intensity levels using parcel-level data from Austin, Texas.

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A note on composite likelihood inference and model selection

Cited by 287 publications

References 35 publications

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

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

Information criteria on multimodel selection of parametric regression: Biological applications

A spatial panel ordered-response model with application to the analysis of urban land-use development intensity patterns

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