Pairwise likelihood inference for ordinal categorical time series

Varin, Cristiano; Vidoni, Paolo

doi:10.1016/j.csda.2006.09.009

Cited by 35 publications

(40 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…That is, the additional inclusion of pairs separated by a distance larger than m * does not only increase the computational cost, but may also reduce the efficiency of the estimators. Numerical illustrations of this phenomena were given by Varin and Vidoni (2006) and Varin and Vidoni (2008) for nonlinear time series, Varin and Czado (2008) for longitudinal data analysis as well as by Bevilacqua et al (2007) and Apanasovich et al (2008) for spatial processes.…”

Section: Time Seriesmentioning

confidence: 99%

On composite marginal likelihoods

Varin

2008

AStA

Self Cite

252

227

View full text Add to dashboard Cite

show abstract

Section: Time Seriesmentioning

confidence: 99%

On composite marginal likelihoods

Varin

2008

AStA

Self Cite

252

227

View full text Add to dashboard Cite

show abstract

“…A pairwise likelihood estimation, where the likelihood function is defined as the product of the bivariate likelihoods, is proposed in Katsikatsou et al (2012) for SEM for ordinal variables and in Katsikatsou (2013) for continuous and ranking data. PML estimation has been developed for panel models of ordered-responses (Bhat et al, 2010), latent variable models for ordinal longitudinal responses (Vasdekis et al, 2012), autoregressive ordered probit models (Varin & Vidoni, 2006), longitudinal mixed Rasch models (Feddag & Bacci, 2009), mixed models for joint modelling of multivariate longitudinal profiles (Fieuws & Verbeke, 2006), analysis of variance models (Lele & Taper, 2002), generalized linear models with crossed random effects (Bellio & Varin, 2005), spatial models with binary data (Heagerty & Lele, 1998), and spatial generalized linear mixed models (see also the special issue of Statistica Sinica, Vol 21(1), 2011, for more areas of application).…”

Section: Introductionmentioning

confidence: 99%

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

Katsikatsou

Moustaki

2016

Psychometrika

View full text Add to dashboard Cite

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 .

show abstract

“…These applications include the analysis of correlated binary data (Le Cessie and Van Houwelingen 1994; Kuk and Nott 2000), binary spatial data (Heagerty and Lele 1998) and random set models for binary images (Nott and Rydén 1999). More recent applications include serially correlated count data (Henderson and Shimakura 2003), estimation of recombination rates from pairs of loci in gene sequences (Fearnhead 2003), stochastic geometry for a variety of spatial point process (Guan 2006) and analysis of ordinal categorical time series (Varin and Vidoni 2006). Cox and Reid (2004) also considered the case of a fixed sample size n and provided conditions for the consistency of the MPL estimators when the dimension d of the vectors, and thus the number of pairs, increases.…”

Section: Maximum Pairwise Likelihood Methodsmentioning

confidence: 98%

Pairwise likelihood estimation for multivariate mixed Poisson models generated by Gamma intensities

2008

View full text Add to dashboard Cite

International audienceEstimating the parameters of multivariate mixed Poisson models is an important problem in image processing applications, especially for active imaging or astronomy. The classical maximum likelihood approach cannot be used for these models since the corresponding masses cannot be expressed in a simple closed form. This paper studies a maximum pairwise likelihood approach to estimate the parameters of multivariate mixed Poisson models when the mixing distribution is a multivariate Gamma distribution. The consistency and asymptotic normality of this estimator are derived. Simulations conducted on synthetic data illustrate these results and show that the proposed estimator outperforms classical estimators based on the method of moments. An application to change detection in low-flux images is also investigated

show abstract

Pairwise likelihood inference for ordinal categorical time series

Cited by 35 publications

References 12 publications

On composite marginal likelihoods

On composite marginal likelihoods

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

Pairwise likelihood estimation for multivariate mixed Poisson models generated by Gamma intensities

Contact Info

Product

Resources

About