Results 251 to 260 of about 2,011,711 (312)
Versatile Descent Algorithms for Group Regularization and Variable Selection in Generalized Linear Models. [PDF]
J Comput Graph StatHelwig NE.
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Debiased lasso after sample splitting for estimation and inference in high-dimensional generalized linear models. [PDF]
Can J StatVazquez O, Nan B.
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Deeply-Learned Generalized Linear Models with Missing Data. [PDF]
J Comput Graph StatLim DK, Rashid NU, Oliva JB, Ibrahim JG.
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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
2015
This article provides an introduction into the statistical analysis of neuroimaging data using the general linear model. The analysis allows a flexible use of various models offering a wide range of statistical tests for the analysis of typical neuroimaging experiments. A short introduction to the general linear model is provided using simple examples.
Kiebel, S. +1 more
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This article provides an introduction into the statistical analysis of neuroimaging data using the general linear model. The analysis allows a flexible use of various models offering a wide range of statistical tests for the analysis of typical neuroimaging experiments. A short introduction to the general linear model is provided using simple examples.
Kiebel, S. +1 more
openaire +2 more sources
2007
This chapter presents the general linear model as an extension to the two-sample t-test, analysis of variance (ANOVA), and linear regression. We illustrate the general linear model using two-way ANOVA as a prime example. The underlying principle of ANOVA, which is based on the decomposition of the value of an observed variable into grand mean, group ...
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This chapter presents the general linear model as an extension to the two-sample t-test, analysis of variance (ANOVA), and linear regression. We illustrate the general linear model using two-way ANOVA as a prime example. The underlying principle of ANOVA, which is based on the decomposition of the value of an observed variable into grand mean, group ...
openaire +2 more sources