linear mixed-effects model
The SAEM algorithm for group comparison tests in longitudinal data analysis based on non-linear mixed-effects model.
... Non-linear mixed effects models; SAEM algorithm; sample ...on linear or non-linear mixed-effects models ...to model the evolution of functional markers, for ...
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A comparison of bootstrap approaches for estimating uncertainty of parameters in linear mixed-effects models.
... dom effects and residuals, either in a nonparametric or in a parametric ...random effects into the classical resid- ual bootstrap plays therefore a very important role for bootstrapping in ...
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Extension of the SAEM algorithm to left-censored data in nonlinear mixed-effects model: Application to HIV dynamics model
... Nonlinear mixed-effects models; SAEM algorithm 1 Introduction HIV viral load is a widespread marker of the evolution of HIV infected pa- tients (1); the reduction in HIV viral load is frequently used as the ...
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A mixed-effects model with time reparametrization for longitudinal univariate manifold-valued data
... phase. Mixed effects models provide a rich framework to analyze longitudinal ...random) effects allow to describe the model at the group (respectively sub- ject) level and the distribution of ...
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Robustness of the linear mixed model to misspecied error distribution: Robustness of the linear mixed model
... fixed effects which is the unique objective of the longitudinal analysis in most ...random effects may be biased when the covariance structure is ...random effects estimates or individual predictions ...
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Standard errors of solutions in large scale mixed models, application to linear and curvilinear effects of inbreeding on production traits.
... Key Words: Genetic Evaluation, Selection Index 25 A bivariate quantitative genetic model for a linear Gaussian trait and a survival trait. L. H. Damgaard* and I. R. Korsgaard, Research Centre Foulum, Dept. ...
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A latent mixed-effects model for longitudinal categorical data in Parkinson disease
... Non-linear mixed effect model : 𝜼 !" = 𝜸 𝒊 (4𝒕 !" ) • Estimation of parameters with a Monte Carlo Markov Chain Stochastic Approximation of Expectation-Maximization algorithm (MCMC SAEM) ...
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Misuse of the linear mixed model when evaluating risk factors of cognitive decline.
... two mixed models based on latent processes that handle discrete and bounded outcomes were ...between effects on mean level and on change over time was ...alternative mixed models when studying ...
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Detecting single-trial EEG evoked potential using a wavelet domain linear mixed model: application to error potentials classification
... 1. Introduction Electro- and Magneto-encephalography (respectively, EEG and MEG) are of the rare techniques allowing non-invasive brain investigation with an excellent temporal resolution and, under some conditions, a ...
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New adaptive strategies for nonparametric estimation in linear mixed models
... (2)MAP5, UMR CNRS 8145, Universit´e Paris Descartes, Sorbonne Paris Cit´e, 45 rue des Saints P`eres, 75006 Paris Abstract This paper surveys new estimators of the density of a random effect in linear ...
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Validity of summary statistics-based mixed-effects group fMRI
... a mixed-effects GLM (MFX GLM) where within-subject variance estimates are used and incorporated into per-subject weights or 2) a random-effects General linear model (GLM) (RFX GLM) ...
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Pharmacokinetic similarity of biologics: analysis using nonlinear mixed-effects modeling.
... in model-based approaches and tends to be more acceptable and familiar to Health ...a model-based approach can correct it [29, 30]. Even for linear PK, the interpolation of the AUC between last ...
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Extension to mixed models of the Supervised Component-based Generalised Linear Regression
... ear Mixed Model ...random effects used to introduce the dependence between statistical ...to model Y and capture relevant structural information in ...the model, we propose to maximise ...
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Phenomenological modeling of tumor diameter growth based on a mixed effects model
... the linear growth of diameter kinetics, tumor diameter-based rather than tumor volume-based models have been devel- oped for the phenomenological modeling of tumor ...two linear parts and one ex- ponential ...
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Bivariate linear mixed models using SAS proc MIXED.
... The option "CL" requests confidence limits for the covariance parameter estimates. A Satterthwaite approximation is used to construct limits for all parameters that have a default lower boundary constraint of ...
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Estimating Markov and semi-Markov switching linear mixed models with individual-wise random effects
... random effects, the state sequences are sampled using the “forward-backward” algo- rithm proposed by Gu´edon ...random effects are predicted as previously ...the linear mixed model ...
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Coordination and control of UAV fleets using mixed-integer linear programming
... This approach is shown to avoid entrapment behind ob- stacles, to yield near-optimal performance when comparison with the minimum arrival time found using a fixed [r] ...
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Analyzing Multi-Objective Linear and Mixed Integer Programs by Lagrange Multipliers
... A new method for multi-objective optimization of linear and mixed programs based on Lagrange multiplier methods is developed.. The method resembles, but is distinct [r] ...
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Random thresholds for linear model selection
... The article is organized as follows: in section 2 we introduce the problem and basic notation as well as the proposed test procedure. In section 3 we state and prove theoretical results that justify our procedure, namely ...
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Mixed integer linear programming for quality of service optimization in Clouds
... a Mixed Integer Linear Programming (MILP) ...global mixed-integer non linear programming formulation is presented and a MILP formulation is derived by ...
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