Non linear mixed effect models
Markov and semi-Markov switching linear mixed models for identifying forest tree growth components.
... switching linear mixed model (Chaubert et ...the effect of climatic factors and the inter-individual heterogeneity were not explicitly modeled while in the second case, the length of the successive ...
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The SAEM algorithm for group comparison tests in longitudinal data analysis based on non-linear mixed-effects model.
... Abstract Non-linear mixed-effects models (NLMEMs) are used to improve information gather- ing from longitudinal studies and applied to treatment evaluation in disease evolution studies, such ...
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saemix, an R version of the SAEM algorithm for parameter estimation in nonlinear mixed effect models
... le. Non- linear mixed ee
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Robustness of the linear mixed model to misspecied error distribution: Robustness of the linear mixed model
... Both models exhibit a treatment effect on CD4 counts change, a higher slope for the D4T+DDI group, but interpretation of parameter values is more difficult when the transformed variable is ...the ...
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Parameter Estimation in Nonlinear Mixed Effect Models Using saemix, an R Implementation of the SAEM Algorithm
... The saemix package provides an implementation of one of these recent estimation algorithms, SAEM, in R, to obtain maximum likelihood estimates. It offers an interesting alternative to linearisation-based estimation ...
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A comparison of bootstrap approaches for estimating uncertainty of parameters in linear mixed-effects models.
... A version of the nonparametric bootstrap, which resamples the entire subjects from original data, called the case bootstrap, has been increasingly used for estimating uncertainty of pa- rameters in mixed-effects ...
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New adaptive strategies for nonparametric estimation in linear mixed models
... random effect in linear mixed-effects ...random effect of ...them, non-asymptotic L 2 -risk bounds are established implying estimation rates, much better than the expected deconvolution ...
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Extension to mixed models of the Supervised Component-based Generalised Linear Regression
... more Mixed-SCGLR (with l = 4 as recommended in [ 8 ]) focuses on the main structures in X that contribute to model Y ...0.1, Mixed-SCGLR provides the most precise fixed effect estimates despite the ...
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Estimating Markov and semi-Markov switching linear mixed models with individual-wise random effects
... rainfall effect was computed as β j2 × sd(X) for each state j where sd(X) is the standard deviation of the cumulated rainfalls ...rainfall effect represents the average amplitude of the climatic ...
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Bivariate linear mixed models using SAS proc MIXED.
... The bivariate random effects model was significantly better than two separate univariate random effects models (-25194 vs. -25307, likelihood ratio = 226 with 4 degrees of freedom, p<10 -4 , table 2) showing a ...
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Markov and semi-Markov switching linear mixed models used to identify forest tree growth components.
... switching linear mixed model (SMS-LMM) parameters (state occupancy distributions and marginal observation ...rainfall effect and the variability decomposition are ...
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Toward robust parameterized reduced-order models of non-linear structures using POD
... [7] M. Geradin and D. Rixen. Mechanical vibrations: theory and applications to structural dynamics. John Wiley & Sons Ltd, 1997. [8] M. Meyer and H.G. Matthies. Efficient model reduction in non-linear ...
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Comparison of linear and non-linear soft tissue models with post-operative CT scan in maxillofacial surgery
... surgeon since the use of a soft tissue model in actual surgical practice cannot be considered without an extensive clinical validation. While many models were proposed in the literature, few works propose ...
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Asymptotic derivation of high-order rod models from non-linear 3D elasticity
... Ideal one-dimensional model In this section we explore a formal method for reducing the equilibrium of the prismatic solid, which is a problem in three-dimensional elasticity, to a one-d[r] ...
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Recursive linearly constrained minimum variance estimator in linear models with non-stationary constraints
... a linear superposition of P individual signals ( x ) to noisy data ( v ): y = Hx + v 1 , ...the linear regression problem, where H is a N -by- P matrix and v is a N -dimensional ...
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Linear and non-linear price decentralization
... Colell and Richard [30] was extended in [20,22,32,33,37,38]. In all these papers some variant of ω-properness is assumed and the lattice theoretic arguments are used in a non-trivial way. However, the use of ...
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High Order Scheme for a Non Linear Maxwell System Modelling Kerr Effect
... 101 - 54602 Villers lès Nancy Cedex France Unité de recherche INRIA Rennes : IRISA, Campus universitaire de Beaulieu - 35042 Rennes Cedex France Unité de recherche INRIA Rhône-Alpes : 65[r] ...
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Models for mixed – species forests
... dependent models 35 Individual based distance dependent models, either G&Y or process-based, are useful tools to test hypothesis on the species interactions (light, water, nutrients) and give insights ...
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Modelling finger force produced from different tasks using linear mixed models with lme R function
... The biomechanical data considered in this paper are obtained from a study carried out to understand the coordination patterns of finger forces produced from different tasks. This data cannot be considered independent ...
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Modelling Environmental Effect Dependencies with Principal Component Analysis and Bayesian Dynamic Linear Models
... environmental effect datasets in a model and to increase the prediction capacity, while having the possibility to remove the useless information through the number of PCs ...
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