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Statistics Texts in Statistical Science
“This beautifully written text is unlike any other in statistical science.
It starts at the level of a first undergraduate course in linear algebra
and takes the student all the way up to the graduate level, including Linear Algebra and Matrix
Hilbert spaces. … The book is compactly written and mathematically
rigorous, yet the style is lively as well as engaging. This elegant, Analysis for Statistics Linear Algebra and
sophisticated work will serve upper-level and graduate statistics
education well. All and all a book I wish I could have written.”
—Jim Zidek, University of British Columbia Matrix Analysis for
Linear Algebra and Matrix Analysis for Statistics offers a gradual
exposition to linear algebra without sacrificing the rigor of the subject. Statistics
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.
Features
• Provides in-depth coverage of important topics in linear algebra
that are useful for statisticians, including the concept of rank,
the fundamental theorem of linear algebra, projectors, and
quadratic forms
• Shows how the same result can be derived using multiple
techniques
• Describes several computational techniques for orthogonal
reduction
• Highlights popular algorithms for eigenvalues and eigenvectors
of both symmetric and unsymmetric matrices
• Presents an accessible proof of Jordan decomposition
• Includes material relevant in multivariate statistics and Banerjee
econometrics, such as Kronecker and Hadamard products Roy Sudipto Banerjee
• Offers an extensive collection of exercises on theoretical
concepts and numerical computations Anindya Roy
K10023
K10023_Cover.indd 1 5/6/14 1:49 PM
Linear Algebra and
Matrix Analysis for
Statistics
CHAPMAN & HALL/CRC
Texts in Statistical Science Series
Series Editors
Francesca Dominici, Harvard School of Public Health, USA
Julian J. Faraway, University of Bath, UK
Martin Tanner, Northwestern University, USA
Jim Zidek, University of British Columbia, Canada
Analysis of Failure and Survival Data Data Driven Statistical Methods
P. J. Smith P. Sprent
The Analysis of Time Series: An Introduction, Decision Analysis: A Bayesian Approach
Sixth Edition J.Q. Smith
C. Chatfield Design and Analysis of Experiments with SAS
Applied Bayesian Forecasting and Time Series J. Lawson
Analysis Elementary Applications of Probability Theory,
A. Pole, M. West, and J. Harrison Second Edition
Applied Categorical and Count Data Analysis H.C. Tuckwell
W. Tang, H. He, and X.M. Tu Elements of Simulation
Applied Nonparametric Statistical Methods, B.J.T. Morgan
Fourth Edition Epidemiology: Study Design and
P. Sprent and N.C. Smeeton Data Analysis, Third Edition
Applied Statistics: Handbook of GENSTAT M. Woodward
Analyses Essential Statistics, Fourth Edition
E.J. Snell and H. Simpson D.A.G. Rees
Applied Statistics: Principles and Examples Exercises and Solutions in Statistical Theory
D.R. Cox and E.J. Snell L.L. Kupper, B.H. Neelon, and S.M. O’Brien
Applied Stochastic Modelling, Second Edition Exercises and Solutions in Biostatistical Theory
B.J.T. Morgan L.L. Kupper, B.H. Neelon, and S.M. O’Brien
Bayesian Data Analysis, Third Edition Extending the Linear Model with R:
A. Gelman, J.B. Carlin, H.S. Stern, D.B. Dunson, Generalized Linear, Mixed Effects and
A. Vehtari, and D.B. Rubin Nonparametric Regression Models
Bayesian Ideas and Data Analysis: An J.J. Faraway
Introduction for Scientists and Statisticians A First Course in Linear Model Theory
R. Christensen, W. Johnson, A. Branscum, N. Ravishanker and D.K. Dey
and T.E. Hanson Generalized Additive Models:
Bayesian Methods for Data Analysis, An Introduction with R
Third Edition S. Wood
B.P. Carlin and T.A. Louis Generalized Linear Mixed Models:
Beyond ANOVA: Basics of Applied Statistics Modern Concepts, Methods and Applications
R.G. Miller, Jr. W. W. Stroup
The BUGS Book: A Practical Introduction to Graphics for Statistics and Data Analysis with R
Bayesian Analysis K.J. Keen
D. Lunn, C. Jackson, N. Best, A. Thomas, and Interpreting Data: A First Course
D. Spiegelhalter in Statistics
A Course in Categorical Data Analysis A.J.B. Anderson
T. Leonard Introduction to General and Generalized
A Course in Large Sample Theory Linear Models
T.S. Ferguson H. Madsen and P. Thyregod
An Introduction to Generalized Multivariate Analysis of Variance and
Linear Models, Third Edition Repeated Measures: A Practical Approach for
A.J. Dobson and A.G. Barnett Behavioural Scientists
Introduction to Multivariate Analysis D.J. Hand and C.C. Taylor
C. Chatfield and A.J. Collins Multivariate Statistics: A Practical Approach
Introduction to Optimization Methods and B. Flury and H. Riedwyl
Their Applications in Statistics Multivariate Survival Analysis and Competing
B.S. Everitt Risks
Introduction to Probability with R M. Crowder
K. Baclawski Nonparametric Methods in Statistics with SAS
Introduction to Randomized Controlled Applications
Clinical Trials, Second Edition O. Korosteleva
J.N.S. Matthews Pólya Urn Models
Introduction to Statistical Inference and Its H. Mahmoud
Applications with R Practical Data Analysis for Designed
M.W. Trosset Experiments
Introduction to Statistical Limit Theory B.S. Yandell
A.M. Polansky Practical Longitudinal Data Analysis
Introduction to Statistical Methods for D.J. Hand and M. Crowder
Clinical Trials Practical Multivariate Analysis, Fifth Edition
T.D. Cook and D.L. DeMets A. Afifi, S. May, and V.A. Clark
Introduction to Statistical Process Control Practical Statistics for Medical Research
P. Qiu D.G. Altman
Introduction to the Theory of Statistical A Primer on Linear Models
Inference J.F. Monahan
H. Liero and S. Zwanzig Principles of Uncertainty
Large Sample Methods in Statistics J.B. Kadane
P.K. Sen and J. da Motta Singer Probability: Methods and Measurement
Linear Algebra and Matrix Analysis for A. O’Hagan
Statistics Problem Solving: A Statistician’s Guide,
S. Banerjee and A. Roy Second Edition
Logistic Regression Models C. Chatfield
J.M. Hilbe Randomization, Bootstrap and Monte Carlo
Markov Chain Monte Carlo: Methods in Biology, Third Edition
Stochastic Simulation for Bayesian Inference, B.F.J. Manly
Second Edition Readings in Decision Analysis
D. Gamerman and H.F. Lopes S. French
Mathematical Statistics Sampling Methodologies with Applications
K. Knight P.S.R.S. Rao
Modeling and Analysis of Stochastic Systems, Stationary Stochastic Processes: Theory and
Second Edition Applications
V.G. Kulkarni G. Lindgren
Modelling Binary Data, Second Edition Statistical Analysis of Reliability Data
D. Collett M.J. Crowder, A.C. Kimber,
Modelling Survival Data in Medical Research, T.J. Sweeting, and R.L. Smith
Second Edition Statistical Methods for Spatial Data Analysis
D. Collett O. Schabenberger and C.A. Gotway
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