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The General Linear Model II [PDF]
, 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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Journal of Mathematical Sciences, 1997
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
W. Hatcher, M. Bergeron
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zbMATH Open Web Interface contents unavailable due to conflicting licenses.
W. Hatcher, M. Bergeron
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1981
In Chapter 3 and 4 the method of maximum likelihood was introduced as a general method by which a model could be fitted to data. In Chapter 5 we specialized by restricting ourselves to normally distributed random variables, and to cases where the model is linear in the unknown parameters.
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In Chapter 3 and 4 the method of maximum likelihood was introduced as a general method by which a model could be fitted to data. In Chapter 5 we specialized by restricting ourselves to normally distributed random variables, and to cases where the model is linear in the unknown parameters.
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2008
A partially linear model requires the regression function to be a linear function of a subset of the variables and a nonparametric non-specified function of the rest of the variables. Suppose, for example, that one is interested in estimating the relationship between an outcome variable of interest y and a vector of variables (x, z).
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A partially linear model requires the regression function to be a linear function of a subset of the variables and a nonparametric non-specified function of the rest of the variables. Suppose, for example, that one is interested in estimating the relationship between an outcome variable of interest y and a vector of variables (x, z).
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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.
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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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Journal of Statistical Planning and Inference, 1984
The paper under review is devoted to the comparison of linear models and linear normal models. The first part deals with the case when the covariances are known. It is pointed out that the ordering of two linear models \(L_ 1,L_ 2\) with \(L_ 1\geq L_ 2\) by variances of best linear estimators coincides with the concept of factorization into ...
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The paper under review is devoted to the comparison of linear models and linear normal models. The first part deals with the case when the covariances are known. It is pointed out that the ordering of two linear models \(L_ 1,L_ 2\) with \(L_ 1\geq L_ 2\) by variances of best linear estimators coincides with the concept of factorization into ...
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1982
Suppose that we are to analyze n measurements or observations y i to see how they depend upon q other sets of measurements or observations Fl … F q If F j is considered quantitative, we will refer to it as a variate. If F j is considered qualitative, we will refer to it as a factor, and use the notation n j to denote the number of levels of F j .
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Suppose that we are to analyze n measurements or observations y i to see how they depend upon q other sets of measurements or observations Fl … F q If F j is considered quantitative, we will refer to it as a variate. If F j is considered qualitative, we will refer to it as a factor, and use the notation n j to denote the number of levels of F j .
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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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