Results 291 to 300 of about 2,001,852 (330)
Some of the next articles are maybe not open access.
Parallel computing in linear mixed models
Computational Statistics, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Fulya Gokalp-Yavuz, Barret Schloerke
openaire +1 more source
Linear Equality Constraints in the General Linear Mixed Model
Biometrics, 2001Scientists may wish to analyze correlated outcome data with constraints among the responses. For example, piecewise linear regression in a longitudinal data analysis can require use of a general linear mixed model combined with linear parameter constraints.
Edwards, Lloyd J. +3 more
openaire +3 more sources
Model comparison of generalized linear mixed models
Statistics in Medicine, 2005AbstractGeneralized linear mixed models (GLMMs) have been widely appreciated in biological and medical research. Maximum likelihood estimation has received a great deal of attention. Comparatively, not much has been done on model comparison or hypotheses testing.
Xin-Yuan, Song, Sik-Yum, Lee
openaire +2 more sources
2012
In current clinical research repeated measures in a single subject are common. The problem with repeated measures is, that they are more close to one another than unrepeated measures. If this is not taken into account, then data analysis will lose power.
Ton J. Cleophas, Aeilko H. Zwinderman
openaire +1 more source
In current clinical research repeated measures in a single subject are common. The problem with repeated measures is, that they are more close to one another than unrepeated measures. If this is not taken into account, then data analysis will lose power.
Ton J. Cleophas, Aeilko H. Zwinderman
openaire +1 more source
Testing transformations for the linear mixed model
Computational Statistics & Data Analysis, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Matthew J. Gurka +2 more
openaire +1 more source
2009
In the early 1950s, C.R. Henderson developed mixed model estimation, something he began in the 1940s with his Ph.D. thesis. He wanted to analyze data for a linear model with fixed environmental and random genetic factors in the breeding of swine (Van Vleck, 1998).
openaire +1 more source
In the early 1950s, C.R. Henderson developed mixed model estimation, something he began in the 1940s with his Ph.D. thesis. He wanted to analyze data for a linear model with fixed environmental and random genetic factors in the breeding of swine (Van Vleck, 1998).
openaire +1 more source
2012
Fixed effects and random effects models are introduced with examples. Maximum likelihood (ML), restricted maximum likelihood (REML) and ANOVA methods of estimation of variance components are described, illustrating with the examples of one-way and two-way classification.
openaire +2 more sources
Fixed effects and random effects models are introduced with examples. Maximum likelihood (ML), restricted maximum likelihood (REML) and ANOVA methods of estimation of variance components are described, illustrating with the examples of one-way and two-way classification.
openaire +2 more sources
2013
The simplest form of the linear mixed model is the random-effects model, which represents data using the regression equation: $$\displaystyle{ \mathbf{y}_{i} =\boldsymbol{\alpha } +\mathbf{b}_{i} +\boldsymbol{\epsilon } _{i} (1 \leq i \leq m), }$$ where \(\boldsymbol{\alpha }\), y i , b i , and \(\boldsymbol{\epsilon }_{i}\) are column matrices ...
openaire +1 more source
The simplest form of the linear mixed model is the random-effects model, which represents data using the regression equation: $$\displaystyle{ \mathbf{y}_{i} =\boldsymbol{\alpha } +\mathbf{b}_{i} +\boldsymbol{\epsilon } _{i} (1 \leq i \leq m), }$$ where \(\boldsymbol{\alpha }\), y i , b i , and \(\boldsymbol{\epsilon }_{i}\) are column matrices ...
openaire +1 more source
Linear Mixed Models for Longitudinal Data
Technometrics, 2001(2001). Linear Mixed Models for Longitudinal Data. Technometrics: Vol. 43, No. 3, pp. 375-375.
openaire +1 more source
Mixed Linear Model with Uncertain Paternity
Applied Statistics, 1992Summary: In animal breeding applications, mixed linear models are often used to estimate genetic parameters and to predict the breeding value of sires, under the assumption that paternity can be attributed without error. This paper considers a mixed linear model for situations in which paternity is uncertain.
openaire +2 more sources