Mixed models
Top PDF Mixed models:
Parametric estimation of complex mixed models based on meta-model approach
... approximate mixed meta-models and the maximum likeli- hood estimates obtained with the exact computer ...non-linear mixed model and Section 3 recalls the principles and the main results of the ...
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Mixed models for longitudinal left-censored repeated measures.
... linear mixed models accounting for left-censoring of the response and we showed with an example that they gave the same ...fit mixed models for left-censored repeated measures, one can choose ...
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New adaptive strategies for nonparametric estimation in linear mixed models
... Mixed models with random effects are often used, for example in ...individuals. Mixed models have been widely studied, often with parametric strategies and Gaussian random effects and noise ...
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Intrapopulation recurrent selection by mixed models in papaya of the formosa group.
... Sarah Ola Moreira 1 , Karin Tesch Kuhlcamp 2 , Fabíola Lacerda de Souza Barros 2 , Moises Zucoloto 3 , Alyce Carla Rodrigues Moitinho 4 A bstract – Few cultivars of papaya from the Formosa group are available to ...
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Estimating Markov and semi-Markov switching linear mixed models with individual-wise random effects
... An interesting direction for further research would be to develop the statisti- cal methodology for semi-Markov switching generalized linear mixed models. Since the hidden semi-Markov chain likelihood ...
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Modelling finger force produced from different tasks using linear mixed models with lme R function
... linear mixed model ( Laird and Ware , 1982 ) then provides then a better framework to take correlation between these observations into ac- ...effects, mixed models allow to take into account the ...
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The benefits of selecting phenotype-specific variants for applications of mixed models in genomics
... linear mixed models (LMMs) to problems in genomics include phenotype prediction, correction for confounding in genome-wide association studies, estimation of narrow sense heritability, and testing sets of ...
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Parametric inference for mixed models defined by stochastic differential equations
... time, mixed models described by stochastic differential equations (SDEs) have been introduced in the literature (see [32] or [43] for ...These models are a natural extension of the models ...
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Bivariate linear mixed models using SAS proc MIXED.
... linear mixed models are useful when analyzing longitudinal data of two associated ...linear mixed model including random effects or first-order auto-regressive process and independent measurement ...
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Modelisation and estimation of heterogeneous variances in nonlinear mixed models
... Nonlinear mixed effects models (NLMM) are more and more frequently used for anal- ysis of longitudinal data and repeated measurements in pharmacokinetics, growth and other ...linear mixed ...
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Markov and semi-Markov switching linear mixed models for identifying forest tree growth components.
... Markov models such as for instance hidden Markov tree models; see Durand et ...linear mixed models to take into account non-normally distributed response variables (for instance, number of ...
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Extension to mixed models of the Supervised Component-based Generalised Linear Regression
... Besides, regularisation methods have already been developped for GLMM, in which the random effects allow to model complex dependence structure. Eliot et al. [ 3 ] proposed to extend the classical ridge regression to ...
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Bayesian analysis of growth curves using mixed models defined by stochastic differential equations
... corresponding curves on Figure 1. Such a data set has been previously analyzed by Mignon- Grasteau et al. (1999), Jaffr´ ezic et al. (2006) and Meza et al. (2007), who conluded that, among the standard growth ...
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Optimal sequencing of mixed models with sequence-dependent setups and utility workers on an assembly line
... sequen
ing pro
edures for mixed model assembly lines in just-in-time produ
tion. system[r] ...
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Estimation of linear mixed models with a mixture of distribution for the random-effects
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Markov and semi-Markov switching linear mixed models used to identify forest tree growth components.
... Comparison of the estimated Gaussian hidden semi-Markov chain (GHSMC) parameters (i.e. where the influence of covariates and the inter-individual heterogeneity are not taken into account) with the estimated semi-Markov ...
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Extension of the SAEM algorithm for nonlinear mixed models with 2 levels of random effects.
... [Figure 3 about here.] [Figure 4 about here.] 6 Discussion The main original element of this study is the development of the SAEM algorithm for two- levels non-linear mixed effects models. We extend the ...
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A Quasi-Score Marginal Approach in Generalized Linear Mixed Models
... Unit´e de recherche INRIA Lorraine, Technopˆole de Nancy-Brabois, Campus scientifique, ` NANCY 615 rue du Jardin Botanique, BP 101, 54600 VILLERS LES Unit´e de recherche INRIA Rennes, Ir[r] ...
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Mixture of linear mixed models Application to repeated data clustering
... Unité de recherche INRIA Rhône-Alpes 655, avenue de l’Europe - 38330 Montbonnot-St-Martin France Unité de recherche INRIA Lorraine : LORIA, Technopôle de Nancy-Brabois - Campus scientifi[r] ...
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A comparison of bootstrap approaches for estimating uncertainty of parameters in linear mixed-effects models.
... linear mixed-effects models because of the improvement of estimation for variance ...ear models [21], and was extended for the two levels of variability found in mixed models by Wang et ...
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