Journal ArticleDOI
Pairwise likelihood approach to grouped continuous model and its extension
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TLDR
In this article, a pseudo-likelihood estimation method for the grouped continuous model and its extension to mixed ordinal and continuous data is proposed as an alternative to maximum likelihood estimation, which advocates simply pooling marginal pairwise likelihoods to approximate the full likelihood.About:
This article is published in Statistics & Probability Letters.The article was published on 2005-11-01. It has received 52 citations till now. The article focuses on the topics: Restricted maximum likelihood & Likelihood function.read more
Citations
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A note on pseudolikelihood constructed from marginal densities
David Cox,Nancy Reid +1 more
TL;DR: The asymptotic properties of formal maximum likelihood estimators in applications in which only a single qx1 vector of observations is observed are examined, and conditions under which consistent estimators of parameters result from the approximate likelihood using only pairwise joint distributions are studied.
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On composite marginal likelihoods
TL;DR: This paper presents an overview of the topic of composite marginal likelihoods, a pseudolikelihood constructed by compounding marginal densities, with emphasis on applications.
Posted Content
Fitting Vast Dimensional Time-Varying Covariance Models
TL;DR: In this paper, the authors propose a novel and fast way of estimating models of time-varying covariances that overcome an undiagnosed incidental parameter problem which has troubled existing methods when applied to hundreds or even thousands of assets.
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On weighting of bivariate margins in pairwise likelihood
Harry Joe,Youngjo Lee +1 more
TL;DR: For clustered data, a practically good choice of weight is obtained after study of relative efficiencies for an exchangeable multivariate normal model; they are different from weights that had previously been suggested.
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Partial least squares path modeling using ordinal categorical indicators
TL;DR: A Monte Carlo simulation with different population models shows that OrdPLSc provides almost unbiased estimates, and if all constructs are modeled as common factors, it yields estimates close to those of its covariance-based counterpart, WLSMV, but is less efficient.
References
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Journal ArticleDOI
A Composite Likelihood Approach to Binary Spatial Data
TL;DR: In this article, a computationally simple method for estimation and prediction using binary or indicator data in space is proposed based on pairwise likelihood contributions, and the large-sample properties of the estimators are obtained in a straightforward manner.
Composite likelihood methods for space-time data
TL;DR: An intesive simulation study is performed in order to evaluate the performances of the proposed methods based on composite likelihood that can be used to fit space-time data.
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Maximum likelihood estimation of multivariate polyserial and polychoric correlation coefficients
Wai-Yin Poon,Sik-Yum Lee +1 more
TL;DR: In this article, a method for finding the maximum likelihood estimates of the parameters in a multivariate normal model with some of the component variables observable only in polytomous form is developed.
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A pairwise likelihood approach to analyzing correlated binary data
Anthony Y. C. Kuk,David J. Nott +1 more
TL;DR: The computational advantages of pairwise likelihood relative to competing approaches are discussed, some efficiency calculations are presented and it is argued that when cluster sizes are unequal a weighted couplewise likelihood should be used for the marginal regression parameters, whereas the unweighted pairwiselihood should be use for the association parameters.
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Estimating the Mahalanobis distance from mixed continuous and discrete data.
TL;DR: A method for estimating the Mahalanobis distance between two multivariate normal populations when a subset of the measurements is observed as ordered categorical responses and asymptotic properties of the proposed estimator are developed.