ISBN: 0-470-02353-8
Hardcover
272 pages
May 2006
Description
Given the increasing amount of data arising in modern society,
there is a growing interest in statistical matching methods. This
is driven by the need to combine data from different sources in
order to gain more information about the source. There is a real
need for a good up-to-date book on statistical matching methods,
that also provides guidance to the practitioner on how to apply
the methods to their own problems. This book presents an overview
of the best available methods, in a consistent framework, and
provides a critical assessment of each. It includes a large
number of examples and applications, that enable the reader to
apply the methods in their own work.
Table of Contents
Preface.
1 The Statistical Matching Problem.
2 The Conditional Independence Assumption.
3 Auxiliary Information.
4 Uncertainty in Statistical Matching.
5 Statistical Matching and Finite Populations.
6 Some Preliminary Issues for Statistical Matching.
7 Applications.
A Statistical Methods for Partially Observed Data.
B Loglinear Models.
C Distance Functions.
D Finite Population Sampling.
E R code.
Bibliography.
ISBN: 0-470-04005-X
Paperback
560 pages
March 2006
Description
Presents major advances in exploratory data analysis and robust
regression methods not generally available, and explains the
techniques, relating them to classical methods. Covers the role
of exploratory and robust techniques in the overall data-analytic
enterprise, and new methods such as fitting by organized
comparisons using the square combining table, resistant
nonadditive fits for two-way tables, identifying extreme cells in
a sizable contingency table with probabilistic and exploratory
approaches. Features a chapter on using robust regression in less
technical language than available elsewhere. Offers conceptual
support for each technique.
Table of Contents
Partial table of contents:
Theories of Data Analysis: From Magical Thinking Through
Classical Statistics (P. Diaconis).
Fitting by Organized Comparisons: The Square Combining Table (K.
Godfrey).
Resistant Nonadditive Fits for Two-Way Tables (J. Emerson & G.
Wong).
Three-Way Analysis (N. Cook).
Identifying Extreme Cells in a Sizable Contingency Table:
Probabilistic and Exploratory Approaches (F. Mosteller & A.
Parunak).
Fitting Straight Lines By Eye (F. Mosteller, et al.).
Resistant Multiple Regression, One Variable at a Time (J. Emerson
& D. Hoaglin).
Robust Regression (G. Li).
Checking the Shape of Discrete Distributions (D. Hoaglin & J.
Tukey).
Using Quantiles to Study Shape (D. Hoaglin).
Index.
ISBN: 0-470-01092-4
Hardcover
408 pages
May 2006
Description
Research in robust statistics is flourishing, with new methods
being developed, and different applications considered. However,
there are relatively few books covering robust statistics, and
even less that cover the subject in a comprehensive and
definitive manner. These books in themselves are either out-of-date,
very theoretical in nature, or have little coverage of computing.
This book will fulfil the need for a good up-to-date that
presents a broad overview of the theory of robust statistics,
integrated with applications and computing. It will feature in-depth
coverage of the key methodology, including regression,
multivariate analysis, and time series. It will be illustrated
throughout by a range of examples and applications, and is backed
up by S-Plus programs for implementing the methods described. It
is supported by a Website featuring an S-Plus student edition
download, S-Plus programs, and data sets.
ISBN: 0-470-02594-8
Hardcover
482 pages
April 2006
Probability is a vital measure in numerous disciplines, from
bioinformatics and econometrics to finance/insurance and computer
science. Developed from a successful course, Fundamental
Probability: A Computational Approach provides an engaging and
hands-on introduction to this important topic. Whilst the theory
is explored in detail, this book also emphasises practical
applications, with the presentation of a large variety of
examples and exercises, along with generous use of computational
tools.
Based on international teaching experience with students of
statistics, mathematics, finance and econometrics, the book:
Presents new, innovative material alongside the classic theory.
Goes beyond standard presentations by carefully introducing and
discussing more complex subject matter, including a richer use of
combinatorics, runs and occupancy distributions, various
multivariate sampling schemes, fat-tailed distributions, and
several basic concepts used in finance.
Emphasises computational matters and programming methods via
generous use of examples in MATLAB.
Includes a large, self-contained Calculus/Analysis appendix with
derivations of all required tools, such as Leibniz’ rule,
exchange of derivative and integral, Fubini’s theorem, and
univariate and multivariate Taylor series.
Presents over 150 end-of-chapter exercises, graded in terms of
their difficulty, and accompanied by a full set of solutions
online.
This book is intended as an introduction to the theory of
probability for students in biology, mathematics, statistics,
economics, engineering, finance, and computer science who possess
the prerequisite knowledge of basic calculus and linear algebra.
Table of Contents
ISBN: 0-470-00960-8
Paperback
264 pages
March 2006
Description
A comprehensive guide to simulation methods with explicit
recommendations of methods and algorithms. Covers both the
technical aspects of the subject, such as the generation of
random numbers, non-uniform random variates and stochastic
processes, and the use of simulation. Supported by the relevant
mathematical theory, the text contains a great deal of
unpublished research material, including coverage of the analysis
of shift-register generators, sensitivity analysis of normal
variate generators, analysis of simulation output, and more.
Includes a selection of computer programs.
Table of Contents
Aims of Simulation.
Pseudo-Random Numbers.
Random Variables.
Stochastic Models.
Variance Reduction.
Output Analysis.
Uses of Simulation.
Appendixes.
Index.