Results 251 to 260 of about 11,154,365 (309)
A new <i>p</i>-value based multiple testing procedure for generalized linear models. [PDF]
Stat ComputRilling J, Tang CY.
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TESTING HIGH-DIMENSIONAL REGRESSION COEFFICIENTS IN LINEAR MODELS. [PDF]
Ann StatZhao A, Li C, Li R, Zhang Z.
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Reassessing the validity of using weighted linear models to implement multi-generational GWAS-by-subtraction: a response to Evans et al. [PDF]
BMC Res NotesWoolf B, Gill D, Munafò M, Burgess S.
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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
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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
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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 ...
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INFOR: Information Systems and Operational Research, 1971
AbstractThe paper describes research work carried out in Czechoslovakia concerning the location and size of mills, with particular reference to the lumber industry. Two models are presented, the first being a relatively simple linear model (a generalization of an algorithm for solving the standard transportation problem), and the second a more ...
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AbstractThe paper describes research work carried out in Czechoslovakia concerning the location and size of mills, with particular reference to the lumber industry. Two models are presented, the first being a relatively simple linear model (a generalization of an algorithm for solving the standard transportation problem), and the second a more ...
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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.
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Regression and the Linear Model
1981A key feature in most statistical analyses is a statistical model and it will be helpful to look at examples of some simple models, and then discuss some terminology.
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