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Titlebook: Applying Generalized Linear Models; James K. Lindsey Textbook 1997 Springer Science+Business Media New York 1997 ANOVA.Analysis of varianc

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期刊全称Applying Generalized Linear Models
影响因子2023James K. Lindsey
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学科分类Springer Texts in Statistics
图书封面Titlebook: Applying Generalized Linear Models;  James K. Lindsey Textbook 1997 Springer Science+Business Media New York 1997 ANOVA.Analysis of varianc
影响因子.Applying Generalized Linear Models. describes how generalized linear modelling procedures can be used for statistical modelling in many different fields, without becoming lost in problems of statistical inference. Many students, even in relatively advanced statistics courses, do not have an overview whereby they can see that the three areas - linear normal, categorical, and survival models - have much in common. The author shows the unity of many of the commonly used models and provides the reader with a taste of many different areas, such as survival models, time series, and spatial analysis. This book should appeal to applied statisticians and to scientists with a basic grounding in modern statistics. With the many exercises included at the ends of chapters, it will be an excellent text for teaching the fundamental uses of statistical modelling. The reader is assumed to have knowledge of basic statistical principles, whether from a Bayesian, frequentist, or direct likelihood point of view, and should be familiar at least with the analysis of the simpler normal linear models, regression and ANOVA. The author is professor in the biostatistics department at Limburgs University, Die
Pindex Textbook 1997
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978-1-4757-7111-4Springer Science+Business Media New York 1997
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https://doi.org/10.1007/978-3-642-46511-6Longitudinal data involve observations of responses over time that can be modelled as a stochastic process (Lindsey, 1992, 1993). They differ from most other types of data in that the dependence of present response on past history must be taken into account.
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An Introduction to Phase Diagrams,An event history is observed when, in contrast to survival data, events are not absorbing but repeating, so that a series of events, and the corresponding durations between them, can be recorded for each individual.
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https://doi.org/10.1007/978-94-007-1948-4The classical normal linear models are especially simple mathematically, as compared to other members of the exponential dispersion family, for a number of reasons:
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Growth Curves,Longitudinal data involve observations of responses over time that can be modelled as a stochastic process (Lindsey, 1992, 1993). They differ from most other types of data in that the dependence of present response on past history must be taken into account.
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Normal Models,The classical normal linear models are especially simple mathematically, as compared to other members of the exponential dispersion family, for a number of reasons:
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