Linear Models and Generalizations: Least Squares and Alternatives (Springer Series in Statistics)

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Written by two top statisticians with experience in teaching matrix methods for applications in statistics, econometrics and related areas, this book provides a comprehensive treatment of the latest techniques in matrix algebra. A well-balanced approach to discussing the mathematical theory and applications to problems in other areas is an attractive feature of the book.

It can be used as a textbook in courses on matrix algebra for statisticians, econometricians and mathematicians as well. Some of the new developments of linear models are given in some detail using results of matrix algebra.

Revised and updated with the latest results, this Third Edition explores the theory and applications of linear models. The authors present a unified theory of inference from linear models and its generalizations with minimal assumptions. They not only use least squares theory, but also alternative methods of estimation and testing based on convex loss functions and general estimating equations.

Lecture60 (Data2Decision) Generalized Linear Modeling in R

Highlights of coverage include sensitivity analysis and model selection, an analysis of incomplete data, an analysis of categorical data based on a unified presentation of generalized linear models, and an extensive appendix on matrix theory. This book has hardback covers.


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Ex-library,With usual stamps and markings,In good all round condition. No dust jacket. Rao would be found in almost any statistician's list of five outstanding workers in the world of Mathematical Statistics today.

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His book represents a comprehensive account of the main body of results that comprise modern statistical theory. Cochran "C. Rao is one of the pioneers who laid the foundations of statistics which grew from ad hoc origins into a firmly grounded mathematical science.

Efrom Translated into six major languages of the world, C. Rao's Linear Statistical Inference and Its Applications is one of the foremost works in statistical inference in the literature. Incorporating the important developments in the subject that have taken place in the last three decades, this paperback reprint of his classic work on statistical inference remains highly applicable to statistical analysis. Presenting the theory and techniques of statistical inference in a logically integrated and practical form, it covers: The algebra of vectors and matrices Probability theory, tools, and techniques Continuous probability models The theory of least squares and the analysis of variance Criteria and methods of estimation Large sample theory and methods The theory of statistical inference Multivariate normal distribution Written for the student and professional with a basic knowledge of statistics, this practical paperback edition gives this industry standard new life as a key resource for practicing statisticians and statisticians-in-training.

Doctor of Philosophy, Cambridge University, Doctor of Science, Cambridge University, Doctor of Science honorary , Andhra University, Doctor of Science honorary , Delhi University, Doctor of Science honorary , Osmania University, Doctor of Science honorary , Hyderabad University, Doctor of Science honorary , Athens University, Doctor of Science honorary , Leningrad University, Doctor of Science honorary , Philippines University, Doctor of Science honorary , Tampere University, Doctor of Science honorary , Poznan University, Doctor of Science honorary , Slovak Academy of Sciences, Doctor of Science honorary , Barcelona University, Doctor of Science honorary , Munich University, Doctor of Science honorary , Guelph University, Doctor of Science honorary , Waterloo University, Doctor of Science honorary , Brasilia University, Doctor of Science honorary , Kent State University, Doctor of Science honorary , University Cyprus, Doctor of Science honorary , University Wollongong, Stuart Coles.

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Home Contact us Help Free delivery worldwide. Free delivery worldwide. Bestselling Series. Harry Potter. Popular Features. New Releases. Description Revised and updated with the latest results, this Third Edition explores the theory and applications of linear models. The authors present a unified theory of inference from linear models and its generalizations with minimal assumptions.

They not only use least squares theory, but also alternative methods of estimation and testing based on convex loss functions and general estimating equations. Highlights of coverage include sensitivity analysis and model selection, an analysis of incomplete data, an analysis of categorical data based on a unified presentation of generalized linear models, and an extensive appendix on matrix theory.

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Product details Format Hardback pages Dimensions x x Other books in this series. Add to basket. Weak Convergence and Empirical Processes A. Targeted Learning Mark J. Functional Data Analysis J. Principal Component Analysis Ian T. Design of Observational Studies Paul R. Theory of Statistics Mark J.

Regression Modeling Strategies Jr. Review Text From the reviews of the third edition: "The book contains a massive amount of useful results related to the world of linear models.

Linear Models and Generalizations: Least Squares and Alternatives (Springer Series in Statistics)

I find my life more comfortable when I have this book in my bookshelf while checking whether some results have appeared in the literature. Radhakrishna Rao. This is a very useful book and the authors earn congratulations.