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Advances in Complex Function Theory (Lecture Notes in Mathematics) by W. E. Kirwan (Repost)
Matrix Mathematics - Theory, Facts, and Formulas, Second Edition
Mathematics Probability, Markov Chains, Queues, and Simulation - The Mathematical Basis of Performance Modeling
Algebraic Aspects of Cryptography (Algorithms and Computation in Mathematics) by Neal Koblitz (Repost)
Mathematics Mathematical Foundations of Computer Science 2004 [Repost]
Mathematics Mathematical Logic for Computer Science (3rd edition)
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An Introduction to Ergodic Theory (Graduate Texts in Mathematics) by Peter Walters (Repost)
Mathematics Symmetry Theory in Molecular Physics with Mathematica: A new kind of tutorial book (Repost)
-Mathematics for the Physical Sciences- by Herbert S. Wilf
Mathematics for Elementary Teachers - A Conceptual Approach, 9 edition
Computer-Enabled Mathematics - Integrating Experiment and Theory in Teacher Education
How to Fold It - The Mathematics of Linkages, Origami and Polyhedra
Mathematics Engineering Analysis: Interactive Methods and Programs with FORTRAN, QuickBASIC, MATLAB, and Mathematica [Repost]
Mathematics Maverick Mathematician: The Life and Science of J.E. Moyal
African Mathematics: From Bones to Computers (repost)
Topology (Allyn and Bacon Series in Advanced Mathematics) by James Dugundji
Mathematics Fundamentals of Algebraic Modeling - An Introduction to Mathematical Modeling with Algebra and Statistics, 5 edition
Mathematics LMSST - 24 Lectures on Elliptic Curves (London Mathematical Society Student Texts) by J. W. S. Cassels
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Mathematics Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood (Monographs on Statistics and Applied Probabi
Posted on 2010-04-12
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More Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood (Monographs on Statistics and Applied Probability) Since their introduction in 1972, generalized linear models (GLMs) have proven useful in the generalization of classical normal models. Presenting methods for fitting GLMs with random effects to data, Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood explores a wide range of applications, including combining information over trials (meta-analysis), analysis of frailty models for survival data, genetic epidemiology, and analysis of spatial and temporal models with correlated errors. Written by pioneering authorities in the field, this reference provides an introduction to various theories and examines likelihood inference and GLMs. The authors show how to extend the class of GLMs while retaining as much simplicity as possible. By maximizing and deriving other quantities from h-likelihood, they also demonstrate how to use a single algorithm for all members of the class, resulting in a faster algorithm as compared to existing alternatives. Complementing theory with examples, many of which can be run by using the code supplied on the accompanying CD, this book is beneficial to statisticians and researchers involved in the above applications as well as quality-improvement experiments and missing-data analysis.
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