Providing a self-contained exposition of the theory of linear models, this treatise strikes a compromise between theory and practice, providing a sound theoretical basis while putting the theory to work in important cases. The basic idea of the book is to provide readers with enough background to further explore the theory and applications of linear models on their own. The treatment is not meant to be exhaustive; in particular, it is essentially limited to models with one component of variation.
Providing a self-contained exposition of the theory of linear models, this treatise strikes a compromise between theory and practice, providing a soun...
Highlighting modern computational methods, Applied Stochastic Modelling, Second Edition provides students with the practical experience of scientific computing in applied statistics through a range of interesting real-world applications. It also successfully revises standard probability and statistical theory. Along with an updated bibliography and improved figures, this edition offers numerous updates throughout.
New to the Second Edition
An extended discussion on Bayesian methods
A large number of new exercises
A new appendix on computational...
Highlighting modern computational methods, Applied Stochastic Modelling, Second Edition provides students with the practical experience of scientif...
Focusing on the roles of different segments of DNA, Statistics in Human Genetics and Molecular Biology provides a basic understanding of problems arising in the analysis of genetics and genomics. It presents statistical applications in genetic mapping, DNA/protein sequence alignment, and analyses of gene expression data from microarray experiments.
The text introduces a diverse set of problems and a number of approaches that have been used to address these problems. It discusses basic molecular biology and likelihood-based statistics, along with physical...
Focusing on the roles of different segments of DNA, Statistics in Human Genetics and Molecular Biology provides a basic understand...
Offering deep insight into the connections between design choice and the resulting statistical analysis, Design of Experiments: An Introduction Based on Linear Models explores how experiments are designed using the language of linear statistical models. The book presents an organized framework for understanding the statistical aspects of experimental design as a whole within the structure provided by general linear models, rather than as a collection of seemingly unrelated solutions to unique problems.
The core material can be found in the first thirteen...
Offering deep insight into the connections between design choice and the resulting statistical analysis, Design of Experiments: An Introduc...
Linear Algebra and Matrix Analysis for Statistics offers a gradual exposition to linear algebra without sacrificing the rigor of the subject. It presents both the vector space approach and the canonical forms in matrix theory. The book is as self-contained as possible, assuming no prior knowledge of linear algebra.
The authors first address the rudimentary mechanics of linear systems using Gaussian elimination and the resulting decompositions. They introduce Euclidean vector spaces using less abstract concepts and make connections to systems of linear...
Linear Algebra and Matrix Analysis for Statistics offers a gradual exposition to linear algebra without sacrificing the rigor of t...
