ISBN: 0-471-70354-0
Hardcover
742 pages
July 2006
A resource for probability AND random processes, with hundreds of
worked examples and probability and Fourier transform tables
This survival guide in probability and random processes
eliminates the need to pore through several resources to find a
certain formula or table. It offers a compendium of most
distribution functions used by communication engineers, queuing
theory specialists, signal processing engineers, biomedical
engineers, physicists, and students.
Key topics covered include:
Random variables and most of their frequently used discrete and
continuous probability distribution functions
Moments, transformations, and convergences of random variables
Characteristic, generating, and moment-generating functions
Computer generation of random variates
Estimation theory and the associated orthogonality principle
Linear vector spaces and matrix theory with vector and matrix
differentiation concepts
Vector random variables
Random processes and stationarity concepts
Extensive classification of random processes
Random processes through linear systems and the associated Wiener
and Kalman filters
Application of probability in single photon emission tomography (SPECT)
More than 400 figures drawn to scale assist readers in
understanding and applying theory. Many of these figures
accompany the more than 300 examples given to help readers
visualize how to solve the problem at hand. In many instances,
worked examples are solved with more than one approach to
illustrate how different probability methodologies can work for
the same problem.
Several probability tables with accuracy up to nine decimal
places are provided in the appendices for quick reference. A
special feature is the graphical presentation of the commonly
occurring Fourier transforms, where both time and frequency
functions are drawn to scale.
This book is of particular value to undergraduate and graduate
students in electrical, computer, and civil engineering, as well
as students in physics and applied mathematics. Engineers,
computer scientists, biostatisticians, and researchers in
communications will also benefit from having a single resource to
address most issues in probability and random processes.
Table of Contents
ISBN: 0-471-99891-5
Hardcover
350 pages
October 2006
Description
This book will not only be readily welcomed in the academic study
of classical optimal selection problems but will also make an
impact in areas of modern finance, portfolio management and
discrete Mathematics. The initial problem, that of "the
classical secretary" dealt with simple and generalised
competitive rank problems. However, this and similar problems
have developed into greatly extended and complex studies but are
without treatment in a unified text. You will be offered a number
of new tools and innovative methodologies for solving complex
problems, and challenged by the authors identification of
further, unresolved problems.
ISBN: 0-471-74696-7
Hardcover
408 pages
July 2006
The essentials of regression analysis through practical
applications
Regression analysis is a conceptually simple method for
investigating relationships among variables. Carrying out a
successful application of regression analysis, however, requires
a balance of theoretical results, empirical rules, and subjective
judgement. Regression Analysis by Example, Fourth Edition has
been expanded and thoroughly updated to reflect recent advances
in the field. The emphasis continues to be on exploratory data
analysis rather than statistical theory. The book offers in-depth
treatment of regression diagnostics, transformation,
multicollinearity, logistic regression, and robust regression.
This new edition features the following enhancements:
Chapter 12, Logistic Regression, is expanded to reflect the
increased use of the logit models in statistical analysis
A new chapter entitled Further Topics discusses advanced areas of
regression analysis
Reorganized, expanded, and upgraded exercises appear at the end
of each chapter
A fully integrated Web page provides data sets
Numerous graphical displays highlight the significance of visual
appeal
Regression Analysis by Example, Fourth Edition is suitable for
anyone with an understanding of elementary statistics. Methods of
regression analysis are clearly demonstrated, and examples
containing the types of irregularities commonly encountered in
the real world are provided. Each example isolates one or two
techniques and features detailed discussions of the techniques
themselves, the required assumptions, and the evaluated success
of each technique. The methods described throughout the book can
be carried out with most of the currently available statistical
software packages, such as the software package R.
ISBN: 0-470-09571-7
Paperback
480 pages
August 2006
Description
This valuable book-length treatment of the field offers coverage
of estimation for situations where the model variables are
observed subject to measurement error. Included are regression
models with errors in the variables, latent variable models, and
factor models. This book brings together results from several
areas of application, including discussion of recent results for
nonlinear models and for models with unequal variances. Also
explained are the estimation of true values for the fixed model,
prediction of true values under the random model, model checks,
and the analysis of residuals. Procedures are illustrated with
data drawn from nearly twenty real-data sets.
ISBN: 0-471-21488-4
Hardcover
464 pages
August 2006
A precise and accessible presentation of linear model theory,
illustrated with data examples
Statisticians often use linear models for data analysis and for
developing new statistical methods. Most books on the subject
have historically discussed univariate, multivariate, and mixed
linear models separately, whereas Linear Model Theory:
Univariate, Multivariate, and Mixed Models presents a unified
treatment in order to make clear the distinctions among the three
classes of models.
Linear Model Theory: Univariate, Multivariate, and Mixed Models
begins with six chapters devoted to providing brief and clear
mathematical statements of models, procedures, and notation. Data
examples motivate and illustrate the models. Chapters 7-10
address distribution theory of multivariate Gaussian variables
and quadratic forms. Chapters 11-19 detail methods for
estimation, hypothesis testing, and confidence intervals. The
final chapters, 20-23, concentrate on choosing a sample size.
Substantial sets of excercises of varying difficulty serve
instructors for their classes, as well as help students to test
their own knowledge.
The reader needs a basic knowledge of statistics, probability,
and inference, as well as a solid background in matrix theory and
applied univariate linear models from a matrix perspective.
Topics covered include:
A review of matrix algebra for linear models
The general linear univariate model
The general linear multivariate model
Generalizations of the multivariate linear model
The linear mixed model
Multivariate distribution theory
Estimation in linear models
Tests in Gaussian linear models
Choosing a sample size in Gaussian linear models
Filling the need for a text that provides the necessary
theoretical foundations for applying a wide range of methods in
real situations, Linear Model Theory: Univariate, Multivariate,
and Mixed Models centers on linear models of interval scale
responses with finite second moments. Models with complex
predictors, complex responses, or both, motivate the presentation.