Results 201 to 210 of about 1,556,042 (247)
Uncertainty quantification in high-dimensional linear models incorporating graphical structures with applications to gene set analysis. [PDF]
BioinformaticsTan X, Tan X, Zhang X, Cui Y, Liu X.
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Unlocking Cognitive Analysis Potential in Alzheimer's Disease Clinical Trials: Investigating Hierarchical Linear Models for Analyzing Novel Measurement Burst Design Data. [PDF]
Stat MedWang G +7 more
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Causal Discovery with Generalized Linear Models through Peeling Algorithms. [PDF]
J Mach Learn ResWang M, Shen X, Pan W.
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Correction to 'Probabilistic outlier identification for RNA sequencing generalized linear models'. [PDF]
NAR Genom Bioinformeuropepmc +1 more source
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WIREs Computational Statistics, 2011
AbstractThis article describes log‐linear models as special cases of generalized linear models. Specifically, log‐linear models use a logarithmic link function. Log‐linear models are used to examine joint distributions of categorical variables, dependency relations, and association patterns.
Von Eye, Alexander +2 more
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AbstractThis article describes log‐linear models as special cases of generalized linear models. Specifically, log‐linear models use a logarithmic link function. Log‐linear models are used to examine joint distributions of categorical variables, dependency relations, and association patterns.
Von Eye, Alexander +2 more
openaire +1 more source
Physical Review E, 2003
We study the time-dependent and the stationary properties of the linear Glauber model in a d-dimensional hypercubic lattice. This model is equivalent to the voter model with noise. By using the Green function method, we get exact results for the two-point correlations from which the critical behavior is obtained.
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We study the time-dependent and the stationary properties of the linear Glauber model in a d-dimensional hypercubic lattice. This model is equivalent to the voter model with noise. By using the Green function method, we get exact results for the two-point correlations from which the critical behavior is obtained.
openaire +2 more sources
2014
Chapter Preview . We give a general discussion of linear mixed models and continue by illustrating specific actuarial applications of this type of model. Technical details on linear mixed models follow: model assumptions, specifications, estimation techniques, and methods of inference.
Antonio, K., Zhang, Y.
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Chapter Preview . We give a general discussion of linear mixed models and continue by illustrating specific actuarial applications of this type of model. Technical details on linear mixed models follow: model assumptions, specifications, estimation techniques, and methods of inference.
Antonio, K., Zhang, Y.
openaire +2 more sources