David, H.A., Iowa State University, Ames, IA, USA
Edwards, A.W.F., Cambridge University, UK
(Eds.)Annotated Readings in the History of Statistics
2001. Approx. 255 pp. 2 figs. Hardcover
0-387-98844-0
This collection of classic articles in statistics combined with commentary by the editors will be of
interest to all serious statisticians.
Contents: Introduction.- The Introduction of the Concept of Expectation-Pascal (1654), Huygens
(1657), and Pascal (1665).- The First Example of a Formal Test of Significance - Arbuthnott (1712).- The Evolution of the Principle of Inclusion and Exclusion - Montmort (1713) and Moivre (1756).- The First Example of the Method of Maximum Likelihood - Lambert (1760).- The Use of the Method of Maximum Probability to Derive the Normal Distribution - Gauss (1809).- The Determination of the Accuracy of Observations - Gauss (1816).- The Introduction of Asymptotic Efficiency - Laplace (1818).- The Distributions in Normal Samples of (a) the Sum of Squares about the Population Mean, (b) the Circular Sum of Squares of Successive Differences, and (c) the Circular Serial Correlation Coefficient - Ernst Abbe (1862).- Yule's Paradox (Simpson's Paradox) - Yule (1903).- Beginnings of Extreme- Value Theory - Bortkiewicz (1922) and Mises (1923).- The Evaluation of Tournament Outcomes - Zermelo (1929).- The Evolution of the Concept of Confidence Limits - Fisher (1930), Neyman (1934), and Fisher (1934).
Series: Springer Series in Statistics.
McPherson, G., University of Tasmania, Hobart, Tas., Australia
Applying and Interpreting Statistics
A Comprehensive Guide
2nd ed. 2001. Approx. 670 pp. 69 figs. Hardcover
0-387-95110-5
This book describes the basis, application, and interpretation of statistics, and presents a wide
range of univariate and multivariate statistical methodology. In its first edition it has proved popular across all science and technology based disciplines, including the social sciences, and in areas of commerce. It is used both as a reference on statistical methodology for researchers and
technicians, and as a textbook with particular appeal for graduate classes containing students of
mixed mathematical and statistical background. The book is developed without the use of calculus,
although several self-contained sections containing calculus are included to provide additional
insight for readers who have a calculus background. Based on the author's "Statistics in Scientific
Investigation," the book has been extended substantially in the area of multivariate applications and through the expansion of logistic regression and log linear methodology. It presumes readers have access to a statistical computing package and includes guidance on the application of statistical computing packages. The new edition retains the unique feature of being written from the users' perspective; it connects statistical models and methods to investigative questions and background information, and connects statistical results with interpretations in plain English. In keeping with this approach, methods are grouped by usage rather than by commonality of statistical methodology. Guidance is provided on the choice of appropriate methods. The use of real life examples has been retained and expanded. Using the power of the Internet, expanded reports on the examples are available at a Springer Web site as Word documents. Additionaly, all data sets are available at the Web site as Excel files, and program files and data sets are provided for SAS users and SPSS users. The programs are annotated so users can adapt.
Contents: The Importance of Statistics in an Informatin Based World.- Data: The Factual
Information.- Statistical Models: The Experimenter's View.- Comparing Model and Data.-
Probability: A Fundamental Tool of Statistics.- Some Widely Used Statistical Models.- Some
Important Statistics and Their Sampling Distributions.- Statistical Analysis: The Statisticians'
View.- Examining Proportions and Success Rates.- Model and Data Chekcking.- Questions About
the Average Value.- Comparing Two Groups, Treatments or Processes.- Comparative Studies,
Surveys and Designed Experiments.- Comparing More Than Two Treatment or Groups.- Comparing Mean Response When There Are Three or More Treatments.- Comparing Patterns of Response: Frequency Tables.- Studying Relations Between Variables.- Prediction and Estimation: The Role of Explanatory Variables.- Questions About Variability. - Cause and Effect: Statistical Perspectives.- Studying Changes in Response Over Time.
Series: Springer Texts in Statistics.
Roe, B.P., University of Michigan, Ann Arbor, MI, USA
Probability and Statistics in Experimental Physics 2nd ed. 2001.
Approx. 255 pp. 44 figs. Hardcover
0-387-95163-6
Intended for advanced undergraduates and graduate students, this book is a practical guide to the
use of probability and statistics in experimental physics. The emphasis is on applications and
understanding, on theorems and techniques actually used in research. The text is not a
comprehensive text in probability and statistics; proofs are sometimes omitted if they do not
contribute to intuition in understanding the theorem. The problems, some with worked solutions,
introduce the student to the use of computers; occasional reference is made to routines available in the CERN library, but other systems, such as Maple, can also be used. Topics covered include:
basic concepts; definitions; some simple results independent of specific distributions; discrete
distributions; the normal and other continuous distributions; generating and characteristic functions;
the Monte Carlo method and computer simulations; multi-dimensional distributions; the central limit
theorem; inverse probability and confidence belts; estimation methods; curve fitting and likelihood
ratios; interpolating functions; fitting data with constraints; robust estimation methods. This second
edition introduces a new method for dealing with small samples, such as may arise in search
experiments, when the data are of low probability. It also includes a new chapter on queuing
problems (including a simple, but useful buffer length example). In addition new sections discuss
over- and under-coverage using confidence belts, the extended maximum-likelihood method, the
use of confidence belts for discrete distributions, estimation of correlation coefficients, and the
effective variance method for fitting y = f(x) when both x and y have measurement errors.
Contents: Preface.- Basic Probability Concepts.- Some Initial Definitions.- Some Results of
Specific Distributions.- Discrete Distributions and Combinatorials.- Specific Discrete Distributions.-
The Normal (or Gaussian) Distribution and Other Continuous Distributions.- Generating Functions
and Characteristic Functions.- The Monte Carlo Method: Computer Simulation of Experiments.-
Queueing Theory and Other Probability Questions.- Two Dimensional and Multi-Dimensional
Distributions.- The Central Limit Theorem.- Inverse Probability; Confidence Limits.- Methods for
Estimating Parameters. Least Squares and Maximum Likelihood.- Curve Fitting.- Bartlett S
Function; Estimating Likelihood Ratios Needed for an Experiment.- Interpolating Functions and
Unfolding Problems.- Fitting Data with Correlations and Constraints.- Beyond Maximum Likelihood
and Least Squares; Robust Methods.- References
Series: Undergraduate Texts in Contemporary Physics.
Erdogmus, H., National Research Council, Ottawa, Ont., Canada
Tanir, O., Bell Canada, Montreal, Que., Canada
(Eds.)Advances in Software Engineering
Comprehension, Evaluation and Evolution
2001. Approx. 465 pp. Hardcover
0-387-95109-1
The proposed volume will contain both practically usable research as well as reviews of different
areas of interest in the software engineering literature, such as clone identification. The contents of the various sections will provide a better understanding of known problems as well as detailed
treatment of advanced topics. Consequently the book consolidates the work and findings from
leading researchers in the software research community.
The book is relevant to real-world problems and not merely based on toy examples; many of the
chapters include results and examples of industrial-strength software. A chapter is dedicated to
tools that can readily be used in addressing many of the problems encountered with software.
Some of the key software engineering topics addressed are maintainability, architectural recovery,
code analysis, software migration, empirical studies, and tool support.
Contents: Part I: Empirical Studies: O-O Metrics: Principles and Practice. Experiences
Conducting Studies of the Work Practices of Software Engineers. Towards Assessing the
Usefulness of the TKSee Software Exploration Tool: A Case Study. Comparison of Clones
Occurrence in Java and Modula-3 Software Systems.- Part II: Architectural Recovery: The
SPOOL Approach to Pattern-Based Recovery of Design Components. Evaluation of Approaches to Clustering for Program Comprehension and Remodularization. Automatic Architectural Clustering of Software. Discovering Implicit Inheritance Relations in Non Object-Oriented Code.- Part III: Maintainability: Design Properties and Evolvability of Object-Oriented Systems. Using Textual Redundancy to Study Source Code Maintainability. Building Parallel Applications Using Design Patterns.- Part IV: Tool Support: The SPOOL Design Repository: Architecture, Schema, and Mechanisms. The Software Bookshelf. Dynamic Documents Over the Web. Support for Geographically Dispersed Software Teams. Parsing C++ Code Despite Missing Declarations.
Towards Environment-Retargetable Parser Generators.
Jorgenson, J., City College of New York, NY, USA
Lang, S., Yale University, New Haven, CT, USASpherical Inversion on SLn
2001. Approx. 360 pp. Hardcover
0-387-95115-6
Harish-Chandra's general Plancherel inversion theorem admits a much shorter presentation for
spherical functions. The authors have taken into account contributions by Helgason, Gangolli,
Rosenberg, and Anker from the mid-1960s to 1990. Anker's simplification of spherical inversion on
the Harish-Chandra Schwartz space had not yet made it into a book exposition. Previous
expositions have dealt with a general, wide class of Lie groups. This has made access to the
subject difficult for outsiders, who may wish to connect some aspects with several if not all other
parts of mathematics, and do so in specific cases of intrinsic interest. The essential features of
Harish-Chandra theory are exhibited on SLn(R), but hundreds of pages of background can be
replaced by short direct verifications. The material becomes accessible to graduate students with
especially no background in Lie groups and representation theory. Spherical inversion is sufficient
to deal with the heat kernel, which is at the center of the authors' current research. The book will
serve as a self-contained background for parts of this research.
Contents: Iwasawa Decomposition and Positivity.- Invariant Differential Operators and the Iwasawa
Direct Image.- Characters, Eigenfunctions, Spherical Kernel and W-Invariance.- Convolutions,
Spherical Functions and the Mellin Transform.- Gelfand-Naimark Decomposition and the
Harish-Chandra -Function.- Polar Decomposition.- The Casimir Operator .- The Harish-Chandra
Series for Eigenfunctions of Casimir.- General Inversion.- The Harish-Chandra Schwartz Space
(HCS) and Anker's Proof of Inversion.- Tube Domains and the L (Even Lp) HCS Spaces.- SLn(C).
Series: Springer Monographs in Mathematics.