Results 251 to 260 of about 24,946,429 (302)
, 2021 Some of the next articles are maybe not open access.
Related searches:
Related searches:
Linear Mixed Effect Models for Rehabilitation Research
American Journal of Physical Medicine & Rehabilitation, 2021Abstract The growing emphasis on evidence-based methods in rehabilitation medicine calls for increase in the sophistication of study design and analytic methods across the discipline. To properly evaluate new treatment options, a physiatrist needs to be able to separate treatment effects from parallel changes that occur over time and ...
J W, Hamner, Can Ozan, Tan
openaire +2 more sources
2007
Statistical models provide a framework in which to describe the biological process giving rise to the data of interest. The construction of this model requires balancing adequate representation of the process with simplicity. Experiments involving multiple (correlated) observations per subject do not satisfy the assumption of independence required for ...
Ann L, Oberg, Douglas W, Mahoney
openaire +2 more sources
Statistical models provide a framework in which to describe the biological process giving rise to the data of interest. The construction of this model requires balancing adequate representation of the process with simplicity. Experiments involving multiple (correlated) observations per subject do not satisfy the assumption of independence required for ...
Ann L, Oberg, Douglas W, Mahoney
openaire +2 more sources
2012
In Chap.10, we presented linear models (LMs) models with fixed effects for correlated data. They are examples of population-averaged models, because their mean-structure parameters can be interpreted as effects of covariates on the mean value of the dependent variable in the entire population. The association between the observations in a dataset was a
Andrzej Gałecki, Tomasz Burzykowski
openaire +1 more source
In Chap.10, we presented linear models (LMs) models with fixed effects for correlated data. They are examples of population-averaged models, because their mean-structure parameters can be interpreted as effects of covariates on the mean value of the dependent variable in the entire population. The association between the observations in a dataset was a
Andrzej Gałecki, Tomasz Burzykowski
openaire +1 more source
Linear Transformations of Linear Mixed-Effects Models
The American Statistician, 1997Abstract A number of articles have discussed the way lower order polynomial and interaction terms should be handled in linear regression models. Only if all lower order terms are included in the model will the regression model be invariant with respect to coding transformations of the variables.
Christopher H. Morrell +2 more
openaire +1 more source
Influence analysis for linear mixed‐effects models
Statistics in Medicine, 2004AbstractIn this paper, we extend several regression diagnostic techniques commonly used in linear regression, such as leverage, infinitesimal influence, case deletion diagnostics, Cook's distance, and local influence to the linear mixed‐effects model.
Eugene, Demidenko, Therese A, Stukel
openaire +2 more sources
A Linear Mixed‐Effects Model for Multivariate Censored Data
Biometrics, 2000Summary.We apply a linear mixed‐effects model to multivariate failure time data. Computation of the regression parameters involves the Buckley‐James method in an iterated Monte Carlo expectation‐maximization algorithm, wherein the Monte Carlo E‐step is implemented using the Metropolis‐Hastings algorithm.
Pan, Wei, Louis, Thomas A.
openaire +2 more sources
2011
This chapter expands on linear models through the introduction of random effects, where the independent variables in the model include those variables that are fixed and those that vary across subjects. The general linear mixed effects model (LMEM) is introduced as methods for parameter estimation – maximum likelihood and restricted maximum likelihood.
openaire +1 more source
This chapter expands on linear models through the introduction of random effects, where the independent variables in the model include those variables that are fixed and those that vary across subjects. The general linear mixed effects model (LMEM) is introduced as methods for parameter estimation – maximum likelihood and restricted maximum likelihood.
openaire +1 more source
Generalized Multi-linear Mixed Effects Model
2016Recently, many applications tend to find common and distinctive features from a group of datasets, of which distributions and structures are generally various. However, most existing methods can just cope with specific problems with fixed distributions and structures.
Chao Li 0013 +4 more
openaire +2 more sources
Mixed effect exponential linear model
Communications in Statistics - Theory and Methods, 1992A theory is developed for the estimation of linear functions of unknown parameters , hypothetical variables and a parameter θ in a mixed auss-Markoff linear model, , where each component is assumed to follow independent exponential distribution with parameter θ.
S.R. Patel, N.P. Patel
openaire +1 more source