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Theory of Statistical Inference
Taschenbuch von Anthony Almudevar
Sprache: Englisch

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Beschreibung
The purpose of applying mathematical theory to the theory of statistical inference is to make it simpler and more elegant. Theory of Statistical Inference is concerned with the development of a type of optimization theory which can be used to inform the choice of statistical methodology.
The purpose of applying mathematical theory to the theory of statistical inference is to make it simpler and more elegant. Theory of Statistical Inference is concerned with the development of a type of optimization theory which can be used to inform the choice of statistical methodology.
Über den Autor

Anthony Almudevar is an Associate Professor of Biostatistics and Computational Biology at the University of Rochester. His research interests include statistical methodology, graphical models, bioinformatics, optimization and control theory. Other published volumes include Almudevar A (2014) Approximate Iterative Algorithms,CRC Press, and Statistical Modeling for Biological Systems: In Memory of Andrei Yakovlev, Anthony Almudevar, David Oakes and Jack Hall, editors (2020), Springer.

Inhaltsverzeichnis

1 Distribution Theory 2 Multivariate Distributions 3 Statistical Models 4 Methods of Estimation 5 Hypothesis Testing 6 Linear Models 7 Decision Theory 8 Uniformly Minimum Variance Unbiased (UMVU) Estimation 9 Group Structure and Invariant Inference 10 The Neyman-Pearson Lemma 11 Limit Theorems 12 Large Sample Estimation - Basic Principles 13 Asymptotic Theory for Estimating Equations 14 Large Sample Hypothesis Testing A Parametric Classes of Densities B Topics in Linear Algebra C Topics in Real Analysis and Measure Theory D Group Theory

Über den Autor

Anthony Almudevar is an Associate Professor of Biostatistics and Computational Biology at the University of Rochester. His research interests include statistical methodology, graphical models, bioinformatics, optimization and control theory. Other published volumes include Almudevar A (2014) Approximate Iterative Algorithms,CRC Press, and Statistical Modeling for Biological Systems: In Memory of Andrei Yakovlev, Anthony Almudevar, David Oakes and Jack Hall, editors (2020), Springer.

Inhaltsverzeichnis

1 Distribution Theory 2 Multivariate Distributions 3 Statistical Models 4 Methods of Estimation 5 Hypothesis Testing 6 Linear Models 7 Decision Theory 8 Uniformly Minimum Variance Unbiased (UMVU) Estimation 9 Group Structure and Invariant Inference 10 The Neyman-Pearson Lemma 11 Limit Theorems 12 Large Sample Estimation - Basic Principles 13 Asymptotic Theory for Estimating Equations 14 Large Sample Hypothesis Testing A Parametric Classes of Densities B Topics in Linear Algebra C Topics in Real Analysis and Measure Theory D Group Theory

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