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Use generalized linear models or generalized partially linear models?
Statistics and Computing, 2023zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Li, Xinmin +3 more
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MHD linear generator modelling
IEEE Transactions on Appiled Superconductivity, 1995The performance of typical magnetohydrodynamic (MHD) linear generators are evaluated as function of the excitation magnetic field profile. Using a three dimensional (3D) lumped parameter model, able to simulate all major physical MHD energy conversion phenomena, a parametric analysis has been pointed out for various saddle shaped superconducting (SC ...
GERI, Alberto +2 more
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2013
The linear models introduced in Chap. 4 deal with continuous response variables—such as height and crop yield—and continuous or discrete explanatory variables. For example, under a normal linear model, the responses \(\{Y _{i}\}\) are independent of each other, and each has a normal distribution with mean \(\mu _{i} = \mathbf{x}_{i}^{\top }\boldsymbol{\
Stefany Coxe +2 more
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The linear models introduced in Chap. 4 deal with continuous response variables—such as height and crop yield—and continuous or discrete explanatory variables. For example, under a normal linear model, the responses \(\{Y _{i}\}\) are independent of each other, and each has a normal distribution with mean \(\mu _{i} = \mathbf{x}_{i}^{\top }\boldsymbol{\
Stefany Coxe +2 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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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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American Journal of Orthodontics and Dentofacial Orthopedics, 2023
Tomasz Burzykowski +3 more
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Tomasz Burzykowski +3 more
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2020
Linear regression is based on the premise that the model is linear in parameters; a set of methods called “generalized linear models” relies on transformations of models that make them linear in parameters; however, the solution to estimation equations is often dependent on numerical approximations; Some more common and important generalized linear ...
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Linear regression is based on the premise that the model is linear in parameters; a set of methods called “generalized linear models” relies on transformations of models that make them linear in parameters; however, the solution to estimation equations is often dependent on numerical approximations; Some more common and important generalized linear ...
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Introducing the generalized linear model: general linear model
2019This chapter reviews the generalized linear model (GLZM), which is an extremely useful and increasingly popular framework approach to analysing data. Since it relies on making assumptions about the distribution of data, it is parametric. In particular, the chapter looks at the general linear model (GLM), a sub-framework of the generalized linear model ...
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1978
In the two preceding chapters we have set forth, in some detail, the estimation of parameters and the properties of the resulting estimators in the context of the standard GLM. We recall that rather stringent assumptions were made relative to the error process and the explanatory variables.
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In the two preceding chapters we have set forth, in some detail, the estimation of parameters and the properties of the resulting estimators in the context of the standard GLM. We recall that rather stringent assumptions were made relative to the error process and the explanatory variables.
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2020
Second-level analyses allow researchers to make inferences about properties of groups or populations, by generalizing from the observations of only a subset of subjects in a study. This chapter describes the mathematics behind the General Linear Model (GLM), the approach used in CONN for all second-level analyses of functional connectivity measures ...
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Second-level analyses allow researchers to make inferences about properties of groups or populations, by generalizing from the observations of only a subset of subjects in a study. This chapter describes the mathematics behind the General Linear Model (GLM), the approach used in CONN for all second-level analyses of functional connectivity measures ...
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