Centring the predictor in mixed-effects models
Longitudinal mixed-effects models for latent cognitive function
... A mixed-effects regression model with a bent-cable change-point predic- tor is formulated to describe potential decline of cognitive function over time in the older ...change-point predictor for non-linear ...
26
The prediction accuracy of dynamic mixed-effects models in clustered data
... patient-level predictor; X 2ij , an unknown patient-level predictor; and N i , the size of the ...of models with fixed effects only, as well as those with random intercepts and random ...These ...
21
Mixed-effects regression models
... The method is easier than using the approach of Clark (1973) Results can be generalized across subjects and items Mixed-effects models are robust to missing data (Baayen, 2008, p. 266) We can easily ...
66
Fitting Mixed Effects Models with Big Data
... Fitting Mixed Effects Models with Big Data by Jingyi He As technology evolves, big data bring us great opportunities to identify patterns which were infeasible to identify from observations ...Linear ...
156
Structural identifiability and indistinguishability in mixed effects models
... There are a several advantages of using mathematical modelling when devel- oping new pharmaceutical drugs. Firstly, modelling can be used to characterise the properties of a drug. With the use of modelling and ...
245
Conditional Akaike information for mixed-effects models
... In addition, the can compare nonnested models. When the model under consideration contains random e ffects, the definition of the is not straightforward. What likelihood should be used? Should the random e ...
Bayesian linear mixed models with polygenic effects
... the mixed model very much the same as in models for families where the relationship matrix is built on familial ...the models appropriate for any samples with typed DNA ...
26
Diagnosing misspecification of the random-effects distribution in mixed models
... random effects in mixed models follow a mul- tivariate normal distribution, making likelihood-based inferences more feasible theo- retically and ...
28
Diagnosing misspecification of the random-effects distribution in mixed models
... random effects in mixed models follow a mul- tivariate normal distribution, making likelihood-based inferences more feasible theo- retically and ...
27
Bayesian inference for diffusion-driven, mixed-effects models
... driven mixed-effects models allow the quantification of both between and within individual ...such models using discrete-time data that may be incomplete and subject to measurement error is a ...
30
Bayesian inference for stochastic differential mixed-effects models
... Picchini et al. (2010) propose a procedure for obtaining approximate maximum likelihood estimates for SDMEM parameters based on a two step approach: they use a closed-form Hermite expansion (A¨ıt-Sahalia, 2002, 2008) to ...
176
FIXED AND MIXED-EFFECTS MODELS FOR MULTI-WATERSHED EXPERIMENTS
... regression models are computed, relating a response in two similar watersheds, before and after a land treatment is applied to one of ...real effects, especially for large, infrequent ...
8
Introduction to Population Pharmacokinetic / Pharmacodynamic. Mixed Effects Models
... statistical models to address diverse questions in the pharmaceutical ...Nonlinear mixed effects models have been the primary analysis framework for population-based pharmacometric modeling, ...
30
Bayesian Inference for Diffusion-Driven Mixed-Effects Models
... Picchini et al. ( 2010 ) propose a procedure for obtaining approximate maximum like- lihood estimates for SDMEM parameters based on a two step approach; they use a closed-form Hermite expansion (A¨ıt-Sahalia, 2002 , 2008 ...
30
Permutation and Bayesian tests for testing random effects in linear mixed-effects models.
... 7. Conclusions and discussion The last two paragraphs of the introduction clarify the importance and novelty of our proposed permutation test based on the likelihood ratio statistic, as well as the need for its ...
24
Testing random effects in linear mixed‐effects models with serially correlated errors
... Linear mixed-effects model; Longitudinal data; Permutation test; Random effects; Serial correlation; 1 Introduction Longitudinal, panel, and clustered data arise in medical, economical, and ...
15
Adaptive estimation of marginal random-effects densities in linear mixed-effects models
... Here, we modify the procedure and improve the upper bound. Moreover we prove oracle risk bounds for the adaptive estimators of f α and f β in presence of unknown noise. These results are difficult and new. We also derive ...
27
FORECASTING CALL CENTER ARRIVALS: FIXED-EFFECTS, MIXED-EFFECTS, AND BIVARIATE MODELS
... §7. One potential difficulty in that computational difficulties are bound to arise when estimating multivariate models involving many different call types. As indicated in §1, forecasting call center arrivals is ...
33
An Optimization Approach for the Parameter Estimation of the Nonlinear Mixed Effects Models
... mented in pro nlmixed. Sine eah of the optimizers requires dierent deriv atives, some omputational eÆienies an be gained. Eah optimization method employs one or more onvergene riteria that determine when it has onverged. ...
161
Uncertainty Quantification for Mixed-Effects Models with Applications in Nuclear Engineering.
... Whereas mixed-effects models are used throughout many areas of science, there are limited tools for performing uncertainty quantification on these types of ...to mixed-effects ...
102