Morton, S., RAND Statistics Group, Santa Monica, CA, USA
Rolph, J., University of Southern California, Los Angeles, CA, USA
(Eds.)

Public Policy and Statistics
Case Studies from RAND

2000. Approx. 305 pp. 36 figs.
0-387-98777-0

A critical yet constructive description of the rich analytical techniques and substantive applications
that typify how statistical thinking has been applied at the RAND Corporation over the past two
decades. Case studies of public policy problems are useful for teaching because they are familiar:
almost everyone knows something abut health insurance, global warming, and capital punishment,
to name but a few of the applications covered in this casebook. Each case study has a common
format that describes the policy questions, the statistical questions, and the successful and the
unsuccessful analytic strategies. Readers should be familiar with basic statistical concepts
including sampling and regression. While designed for statistics courses in areas ranging from
economics to health policy to the law at both the advanced undergraduate and graduate levels,
empirical researchers and policy-makers will also find this casebook informative.

Contents: How to Apply Statistics to Public Policy Problems.- Challenges in Designing and
Analyzing Social Experiments: School-Based Drug Prevention.- The Health Insurance Experiment
Design with the Finite Selection Model.- Sampling Difficult Populations.- Developing and Applying
Effective Data Graphics: Supply Delays for F-14 Jet Engine Repair Parts.- Is There Periodicity in
the Global Mean Temperature Series?- Assessing the Statistical Evidence of Racial Bias in Capital
Cases.- Are Impaired Physicians More Malpractice-Prone?- Comparing Hospital Mortality Rates:
Adjusting For Casemix and Sample Size.- Reconciling Eye Care Provider Supply and Demand.-
Evaluating Block Grant Formulae: Estimating the Need for Substance Abuse Treatment by State.

Series: Statistics for Social Science and Public Policy.

Nolan, D., University of California, Berkeley, CA, USA
Speed, T.P., University of California, Berkeley, CA, USA

Stat Labs
Mathematical Statistics Through Applications

2000. Approx. 335 pp. 45 figs.
0-387-98974-9

This book integrates the theory of statistics with the practice of statistics through a series of case
studies. Each lab introduces a problem, provides some scientific background, suggests
investigations for the data, and provides a summary of the theory used in the investigations. The
text is aimed at upper-division students.

Contents: Maternal Smoking and Infant Health.- Who Plays Video Games.- Minnesota Radon
Levels.- Patterns in DNA.- Can You Taste the Difference?- HIV Infection in Hemophiliacs.-
Dungeness Crab Growth.- Calibrating a Snow Gauge.- Hispanic Voting Behavior.- Maternal
Smoking and Infant Health (Continued).- A Mouse Model for Down Syndrome.- Helicopter Design.-
Writing Lab Reports.- Probability Appendix.

Series: Springer Texts in Statistics.

Stefanescu, G., University of Bucharest, Romania

Network Algebra

2000. XVI, 402 pp.
1-85233-195-X

Network Algebra considers the algebraic study of networks and their behaviour. It contains
general results on the algebraic theory of networks, recent results on the algebraic theory of
models for parallel programs, as well as results on the algebraic theory of classical control
structures. The results are presented in a unified framework of the calculus of flownomials, leading
to a sound understanding of the algebraic fundamentals of the network theory. Network Algebra
will be of interest to anyone interested in network theory or its applications and provides them with
the results needed to put their work on a firm basis. Graduate students will also find the material
within this book useful for their studies.

Contents: An Introduction to Network Algebra: Short Overview on the key results. Network
Algebra and its applications.- Relations, Flownomials and Abstract Networks: Networks modulo
graph isomorphism. Algebraic models for branching constants. Network behaviour. Elgot theories.
Kleene theories.- Algebraic Theory of Special Networks: Flowchart schemes. Automata.
Process Algebra. Dataflow Networks. Petri Nets.- Towards an Algebraic Theory for Software
Components: Mixed Network Algebra. Related Calculi, Closing Remarks.- Appendices.-
Bibliography.- List of Tables.- List of Figures.- Index.

Series: Discrete Mathematics and Theoretical Computer Science.

Weihrauch, K., FernUniversitat Hagen, Germany

Introduction to Computable Analysis

2000. X, 281 pp.
3-540-66817-9


Is the exponential function computable? Are union and intersection of closed sets in the real plain
computable? Are differentiation and integration computable operators? Is zero finding for complex
polynomials computable? Is the Mandelbrot set decidable? And in case of computability, what is
the computational complexity? Computable analysis supplies exact definitions for these and many
other similar questions and tries to solve them. - Merging fundamental concepts of analysis and
recursion theory to a new exciting theory, this book provides a solid fundament for studying various
aspects of computability and complexity in analysis. It is the result of an introductory course given
for many years and is written in a style suitable for graduate-level and senior students in computer
science or mathematics. Many examples illustrate the new concepts while numerous exercises of
varying difficulty extend the material and stimulate readers to work actively on the text.

Keywords: Computable analysis, computability in analysis, complexity in analysis, computable
real functions

Series: Texts in Theoretical Computer Science. An EATCS Series.

Liptser, R., Tel Aviv University, Israel
Shiryaev, A.N., Steklov Mathematical Institute, Moscow, Russia

Statistics of Random Processes II
Applications

2nd, rev. and exp. ed. 2000. X, 402 pp.
3-540-63928-4

The subject of these two volumes is non-linear filtering (prediction and smoothing) theory and its
application to the problem of optimal estimation, control with incomplete data, information theory,
and sequential testing of hypothesis. The book is not only addressed to mathematicians but should
also serve the interests of other scientists who apply probabilistic and statistical methods in their
work. The theory of martingales presented in the book has an independent interest in connection
with problems from financial mathematics.
In the second edition, the authors have made numerous corrections, updating every chapter, adding
two new subsections devoted to the Kalman filter under wrong initial conditions, as well as a new
chapter devoted to asymptotically optimal filtering under diffusion approximation. Moreover, in each
chapter a comment is added about the progress of recent years.

Keywords: Martingale, Filtering, Conditionally Gaussian, Point Process, Incomplete Data Control

Contents: Conditionally Gaussian processes.- Optimal nonlinear filtering, interpolation, and
extrapolation of components of conditionally Gaussian processes.- Conditionally Gaussian
sequences:filtering and related problems.- Application of filtering equations to problems of statistics
of random sequences.- Linear Estimation of random processes.- Application of optimal nonlinear
filtering equations to some problems in control theory and information theory.- Parameter
estimation and testing of statistical hypotheses for diffusion type processes.- Random point
processes: Stieltjes stochastic integrals.- The structure of local martingales, absolute continuity of
measures for point processes, and filtering.- Asymptotically optimal filtering

Series: Applications of Mathematics.VOL. 6

Sprott, D.A., University of Waterloo, Ont., Canada

Statistical Inference in Science

2000. Approx. 265 pp. 49 figs.
0-387-95019-2


A treatment of the problems of inference associated with experiments in science, with the
emphasis on techniques for dividing the sample information into various parts, such that the diverse
problems of inference that arise from repeatable experiments may be addressed. A particularly
valuable feature is the large number of practical examples, many of which use data taken from
experiments published in various scientific journals. This book evolved from the author own courses
on statistical inference, and assumes an introductory course in probability, including the calculation
and manipulation of probability functions and density functions, transformation of variables and the
use of Jacobians. While this is a suitable text book for advanced undergraduate, Masters, and
Ph.D. statistics students, it may also be used as a reference book.

Contents: Introduction.- The Likelihood Function.- Division of Sample Information I.- Division of
Sample Information II.- Estimation Statements.- Tests of Significance.- The Location-Scale Pivotal
Model.- The Gauss Linear Model.- Maximum Likelihood Estimation.- Controlled Experiments.-
Problems.

Series: Springer Series in Statistics.