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Generalized linear mixed models with Gaussian mixture random effects: Inference and application

Lanfeng Pan, Yehua Li, Kevin He, Yanming Li, Yi Li
2019 Journal of Multivariate Analysis  
We propose a new class of generalized linear mixed models with Gaussian mixture random effects for clustered data.  ...  By taking into account patient-level risk factors and modeling the center effects by a finite Gaussian mixture model, the proposed model provides a convenient framework to study the heterogeneity among  ...  Acknowledgement The authors thank the Editor, the Associate Editor and two anonymous referees for their many helpful comments and constructive suggestions, which lead to significant improvement in the  ... 
doi:10.1016/j.jmva.2019.104555 pmid:32063658 pmcid:PMC7021245 fatcat:fjihfmfrqzau7f2xpkmber6hny

Generalized linear mixed model with a penalized Gaussian mixture as a random effects distribution

Arnošt Komárek, Emmanuel Lesaffre
2008 Computational Statistics & Data Analysis  
While it has been shown that misspecifying the distribution of the random effects has a minor effect in the context of linear mixed models, the conclusion for generalized mixed models is less clear.  ...  A replacement of the normal distribution with a mixture of Gaussian distributions specified on a grid whereby only the weights of the mixture components are estimated using a penalized approach ensuring  ...  Acknowledgments We want to thank Alejandro Jara for his help with Bayesian non-parametric models.  ... 
doi:10.1016/j.csda.2007.10.024 fatcat:6mr6pubhhfdtppqtr7d5phawx4

Mixtures of stochastic differential equations with random effects: Application to data clustering

Maud Delattre, Valentine Genon-Catalot, Adeline Samson
2016 Journal of Statistical Planning and Inference  
The distribution of the random effect φi is a Gaussian mixture distribution, depending on unknown parameters which are to be estimated from the continuous observation of the processes Xi.  ...  A simulation study illustrates our estimation and classification method on various models. A real data analysis is performed on growth curves with convincing results.  ...  The two first clusters mix lines LH and LL, which differ only Concluding remarks In this paper, we study mixed-effects SDEs continuously observed with a multivariate linear random effect in the drift  ... 
doi:10.1016/j.jspi.2015.12.003 fatcat:niydpxqhmndb7a7sx2r5zeewzi

Classical and Bayesian Inference in Neuroimaging: Applications

K.J. Friston, D.E. Glaser, R.N.A. Henson, S. Kiebel, C. Phillips, J. Ashburner
2002 NeuroImage  
(ii) The relationship between fixed-and random-effect analyses.  ...  In Friston et al. ((2002) Neuroimage 16: 465-483) we introduced empirical Bayes as a potentially useful way to estimate and make inferences about effects in hierarchical models.  ...  In Penny and Friston (2002) we show how mixtures of general linear models can be estimated using EM.  ... 
doi:10.1006/nimg.2002.1091 pmid:12030833 fatcat:yahnyaaeizh6fk4i22eyy3zhoy

Parameter estimation and inference in the linear mixed model

F.N. Gumedze, T.T. Dunne
2011 Linear Algebra and its Applications  
The paper reviews the linear mixed model with a focus on parameter estimation and inference.  ...  Inferential procedures for the fixed effects, random effects or a combination of both fixed and random effects are also discussed.  ...  We also thank the two reviewers for their comments and suggestions, which led to substantial improvement of the manuscript.  ... 
doi:10.1016/j.laa.2011.04.015 fatcat:mtmdnqkjd5azlbeidlbbpjhq4i

Network Structure Inference, A Survey: Motivations, Methods, and Applications [article]

Ivan Brugere and Brian Gallagher and Tanya Y. Berger-Wolf
2018 arXiv   pre-print
Existing approaches for inferring networks from data are found across many application domains and use specialized knowledge to infer and measure the quality of inferred network for a specific task or  ...  However, current research lacks a rigorous methodology which employs standard statistical validation on inferred models.  ...  Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344 (LLNL-JRNL-703477), and with support of National Science Foundation grants III-1514126 (Berger-Wolf), CNS  ... 
arXiv:1610.00782v4 fatcat:neu7tyamijhixbjv6rasksyasu

Modeling Dependence Using Skew T Copulas: Bayesian Inference and Applications

Michael S. Smith, Quan Gan, Robert Kohn
2010 Social Science Research Network  
In both cases the skew t copula substantially out-performs symmetric elliptical copula alternatives, demonstrating that the skew t copula is a powerful modeling tool when coupled with Bayesian inference  ...  This copula can capture asymmetric and extreme dependence between variables, and is one of the few copulas that can do so and still be used in high dimensions effectively.  ...  Acknowledgments The work of Michael Smith and Robert Kohn was partially supported by Australian Research Council Discovery grants DP0985505 and DP0988579, respectively.  ... 
doi:10.2139/ssrn.1671816 fatcat:grwhppdmorfg5kwxflh2y5ph4i

Impossible Inference in Econometrics: Theory and Applications [article]

Marinho Bertanha, Marcelo J. Moreira
2020 arXiv   pre-print
We conclude by demonstrating impossible inference in multiple economic applications of models with discontinuity and time-series models.  ...  Impossibility in the weak topology is often easier to prove, it is applicable for many widely-used tests, and it is useful for robust hypothesis testing.  ...  Acknowledgements We thank Tim Armstrong, Leandro Gorno, and anonymous referees for helpful comments and suggestions. Bertanha gratefully acknowledges support from ISLA-Notre Dame and CORE-UcLouvain.  ... 
arXiv:1612.02024v5 fatcat:5r4lw3tkhfew3cisnul7ho3eki

Statistical inference of regulatory networks for circadian regulation

Andrej Aderhold, Dirk Husmeier, Marco Grzegorczyk
2014 Statistical Applications in Genetics and Molecular Biology  
, it provides deeper insight into when and why non-linear methods fail to outperform linear ones, it offers improved guidelines on parameter settings in different inference procedures, and it suggests  ...  We assess the accuracy of various state-of-the-art statistics and machine learning methods for reconstructing gene and protein regulatory networks in the context of circadian regulation.  ...  A.A. is supported by the BBSRC and the TiMet project. We are grateful to Andrew Millar, Alexander Pokhilko, and V. Anne Smith for helpful discussions.  ... 
doi:10.1515/sagmb-2013-0051 pmid:24864301 fatcat:6po5swfc25a6lisphtyrtyamwy

A General Algorithm for Approximate Inference and its Application to Hybrid Bayes Nets [article]

Daphne Koller, Uri Lerner, Dragomir Anguelov
2013 arXiv   pre-print
This paper presents a new unified approach that combines approximate inference and the clique tree algorithm, thereby circumventing this limitation.  ...  Many known approximate inference algorithms can be viewed as instances of this approach.  ...  ., and by the generosity of the Powell Foundation and the Sloan Foundation.  ... 
arXiv:1301.6709v1 fatcat:dqzgpf7gujbelkvgwgfmg5k3g4

Bayesian Inference with Posterior Regularization and applications to Infinite Latent SVMs [article]

Jun Zhu, Ning Chen, Eric P. Xing
2014 arXiv   pre-print
idea in combination with a nonparametric Bayesian model for discovering predictive latent features for classification and multi-task learning, respectively.  ...  In this paper, we present regularized Bayesian inference (RegBayes), a novel computational framework that performs posterior inference with a regularization term on the desired post-data posterior distribution  ...  Acknowledgements We thank the anonymous reviewers and the editors for many helpful comments to improve the manuscript. NC and JZ are supported by National Key Foundation R&D Projects  ... 
arXiv:1210.1766v3 fatcat:uagtswctgnh4ze33szjbuntwya

Light and Widely Applicable MCMC: Approximate Bayesian Inference for Large Datasets [article]

Florian Maire, Nial Friel, Pierre Alquier
2015 arXiv   pre-print
LWA-MCMC is a generic and flexible approach, as illustrated by the diverse set of examples which we explore.  ...  Light and Widely Applicable (LWA-) MCMC is a novel approximation of the Metropolis-Hastings kernel targeting a posterior distribution defined on a large number of observations.  ...  Illustration with a probit model: effect of choice of sub-sample We consider a pedagogical example, based on a probit model, to illustrate Proposition 1.  ... 
arXiv:1503.04178v2 fatcat:xsxzej72kvdhfmz2fkzwh3oyfm

Bayesian Inference, Stochastic Simulation and Their Applications in Wireless Communication Systems

Flávio Rainho Ávila, Michel Pompeu Tcheou
2016 Journal of Communication and Information Systems  
This tutorial presents the rudiments of Bayesian statistics and MCMC in general, and discusses their applications in wireless communications in particular.  ...  In addition, the paper addresses the application of Bayesian tools in challenging channel conditions -namely, nonlinear, non-Gaussian, underwater and fast fading channels.  ...  Additionally they are capable of dealing effectively with nonlinear channels and non-Gaussian noise.  ... 
doi:10.14209/jcis.2016.27 fatcat:54rqjs5zxvbwxbugz73fyg52be

Phylodynamic Inference with Kernel ABC and Its Application to HIV Epidemiology

Art F.Y. Poon
2015 Molecular biology and evolution  
A key challenge to phylodynamic inference is quantifying the similarity between two trees in an efficient and comprehensive way.  ...  I validate this "kernel-ABC" method for phylodynamic inference by estimating parameters from data simulated under a simple epidemiological model.  ...  A new set of parameter values ( 0 ) were drawn at random from a truncated multivariate proposal distribution centered at , with a mixture of Gaussian and lognormal distributions with predefined minimum  ... 
doi:10.1093/molbev/msv123 pmid:26006189 pmcid:PMC4540972 fatcat:pe7kkg37lredrhdgkrvr4vuu6u

Characteristic Functions and Their Empirical Counterparts: Geometrical Interpretations and Applications to Statistical Inference

T. W. Epps
1993 American Statistician  
in statistical inference.  ...  This geometrical representation is used to illustrate how various properties of frequency functions and characteristic functions correspond and to illuminate the role of empirical characteristic functions  ...  As for mgf's, the cf of a sum of independent random variables is the product of their cf's, and the cf of a mixture is a convex linear combination of the individual cf's.  ... 
doi:10.2307/2684780 fatcat:twsab6oquvhyfck2tf45bp76ea
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