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Model Checks for Generalized Linear Models
Scandinavian Journal of Statistics, 2002In this paper we propose and study non‐parametric tests for the validity of (composite) Generalized Linear Models with a given parametric link structure, which are based on certain empirical processes marked by the residuals. When properly transformed to their innovation part the resulting test statistics are distribution‐free.
Stute, Winfried, Zhu, Li-Xing
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Linear models for keystream generators
IEEE Transactions on Computers, 1996Consider a keystream generator (KSG) with \(M\) bits of memory. For dimensional reasons, there exists at least one linear function \(L\) of any \(M+1\) consecutive output bits that is not balanced. Under reasonable hypotheses, \(L\) is independent of time, so \(L\) (essentially an \(| M+1| \times 1\) matrix) is a function of the initial state vector ...
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An Introduction to Generalized Linear Models
2008Continuing to emphasize numerical and graphical methods, An Introduction to Generalized Linear Models, Third Edition provides a cohesive framework for statistical modeling. This new edition of a bestseller has been updated with Stata, R, and WinBUGS code as well as three new chapters on Bayesian analysis. Like its predecessor, this edition presents the
Dobson, Annette J, Barnett, Adrian G
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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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GLiM: Generalized linear models
Encyclopedia with Semantic Computing and Robotic Intelligence, 2017Much was written on generalized linear models. The reader is referred to the following books: P. McCullagh and J. A. Nelder, Generalized Linear Models, 2nd edn. (Chapman & Hall, 1989); A. J. Dobson and A. Barnett, An Introduction to Generalized Linear Models, 3 (Chapman & Hall, 2008); C. E. McCulloch and S. R.
Joseph R. Barr, Shelemyahu Zacks
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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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Generalized linear modelling for parasitologists
Parasitology Today, 1997Typically, the distribution of macroparasites over their host population is highly aggregated and empirically best described by the negative binomial distribution. For parasitologists, this poses a statistical provlem, which is often tackled by log-transforming the parasite data prior to analysis by parametric tests. Here, Ken Wilson and Bryan Grenfell
Wilson, Kenneth, Grenfell, Bryan T.
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A nonparametric general linear model
Computers and Biomedical Research, 1972Abstract A matrix formulation of the Kruskal-Wallis analysis of variance is presented. This formulation illustrates the parallel nature of the parametric general linear model and the Kruskal-Wallis model. Using the matrix formulation, it is shown that the Kruskal-Wallis method can be implemented on a digital computer as a special case of a general ...
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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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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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