Results 261 to 270 of about 658,808 (305)
Some of the next articles are maybe not open access.
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
Local Influence in Linear Mixed Models
Biometrics, 1998The linear mixed model has become an important tool in modelling, partially due to the introduction of the SAS procedure MIXED, which made the method widely available to practising statisticians. Its growing popularity calls for data-analytic methods to check the underlying assumptions and robustness. Here, the problem of detecting influential subjects
Lesaffre, Emmanuel, Verbeke, Geert
openaire +2 more sources
2001
Observations often fall into groups or clusters. For example, longitudinal data consist of repeated observations on the same subjects. Hierarchical data sets typically consist of subjects nested in higher level units, such as families or GP practices.
Brian Everitt, Sophia Rabe-Hesketh
openaire +1 more source
Observations often fall into groups or clusters. For example, longitudinal data consist of repeated observations on the same subjects. Hierarchical data sets typically consist of subjects nested in higher level units, such as families or GP practices.
Brian Everitt, Sophia Rabe-Hesketh
openaire +1 more source
Linear and Generalized Linear Mixed Models and Their Applications
Technometrics, 2008(2008). Linear and Generalized Linear Mixed Models and Their Applications. Technometrics: Vol. 50, No. 1, pp. 93-94.
openaire +1 more source
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
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
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
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