3rd ed. 2002. Approx. 500 pp. Hardcover
0-387-95361-2
This textbook provides a wide-ranging introduction to the use and
theory of linear models for analyzing data. The author's emphasis
is on providing a unified treatment of linear models, including
analysis of variance models and regression models, based on
projections, orthogonality, and other vector space ideas. Every
chapter comes with numerous exercises and examples that make it
ideal for a graduate- level course. All of the standard topics
are covered in depth: ANOVA, estimation including Bayesian
estimation, hypothesis testing, multiple comparisons, regression
analysis, and experimental design models. In addition, the book
covers topics that are not usually treated at this level, but
which are important in their own right: balanced incomplete block
designs, testing for lack of fit, testing for independence,
models with singular covariance matrices, variance component
estimation, best linear and best linear unbiased prediction,
collinearity, and variable selection. This new edition includes
discussion of identifiability and its relationship to
estimability, different approaches to the theories of testing
parametric hypotheses and analysis of covariance, additional
discussion of the geometry of least squares estimation and
testing, new discussion of models for experiments with factorial
treatment structures, and a new appendix on possible causes for
getting test statistics that are so small as to be suspicious.
Ronald Christensen is a Professor of Statistics at the University
of New Mexico. He is a Fellow of the American Statistical
Association and the Institute of Mathematical Statistics.
Contents: Introduction.- Estimation.- Testing Hypotheses.- One-Way
ANOVA.- Multiple Comparison Techniques.- Regression Analysis.-
Multifactor Analysis of Variance.- Experimental Design Models.-
Analysis of Covariance.- Estimation and Testing in General Gauss-Markov
Models.- Split Plot Models.- Mixed Models and Variance Components.-
Checking.- Assumptions, Residuals, and Influential Observations.-
Variable Selection and Collinearity.
Series: Springer Texts in Statistics.
2002. X, 186 pp. 58 figs., 6 tabs. Softcover
3-540-42616-7
The present collection of formulas has been composed for students
of economics or management science at universities, colleges and
trade schools. It contains basic knowledge in mathematics,
financial mathematics and statistics in a compact and clearly
arranged form. This volume is meant to be a reference work to be
used by students of undergraduate courses together with a
textbook and by researchers in need of exact statements of
mathematical results. People dealing with practical or applied
problems will also find this collection to be an efficient and
easy-to-use work of reference.
Keywords: Formulas, collection of formulas, mathematical manual,
financial
2002. XI, 492 pp. 84 figs., 65 tabs. Softcover
3-540-43691-X
This book deals with recent developments in classification and
data analysis and presents new topics which are of central
interest to modern statistics. In particular, these include:
classification models and clustering methods, multivariate data
analysis, symbolic data, neural networks and learning devices,
phylogeny and bioinformatics, new software systems for
classification and data analysis, as well as applications in
social, economic, biological, medical and other sciences. The
book presents a long list of useful methods for classification,
clustering and data analysis. By combining theoretical aspects
with practical problems it is designed for researchers as well as
for applied statisticians and will support the fast transfer of
new methodological advances to a wide range of applications.
Keywords: Data Analysis, Classification, Clustering, Neural
Networks, Bioinformatics
Series: Studies in Classification, Data Analysis, and Knowledge
Organization.
2002. Approx. 440 pp. Softcover
0-387-95476-7
This book presents a unified approach for obtaining the limiting
distributions of minimum distance, M and R estimators
corresponding to non-smooth underlying scores in a large class of
dynamic non-linear models including ARCH models. It also
discusses classes of goodness-of-t tests for fitting an error
distribution in some of these models and/or fitting a regression-autoregressive
function without assuming the knowledge of the error distribution.
The main tool is the asymptotic equicontinuity of certain basic
weighted residual empirical processes in the uniform and L2
metrics. The contents of this monograph should be useful to
graduate students and research scholars in statistics,
econometrics, and finance. This book is a an updated edition of
the author's monograph "Weighted Empirical Processes and
Linear Models" (IMS Lecture Notes-Monograph 21, 1992). The
new edition differs from the previous one in many ways. To
mention just a few: It includes asymptotically distribution free
tests for fitting a regression and/or an autoregressive models;
the asymptotic distributions of auto-regression quantiles and
rank scores; and above all the weak convergence of the residual
empirical processes useful in nonlinear ARCH models.
Hira L. Koul is a professor of statistics at Michigan State
University. He is a Fellow of the IMS and an Elected Member of
the International Statistical Institute. He was awarded the
prestigious Humboldt Research Award for Senior Researchers in
1995. He has been on the editorial boards of the Annals of
Statistics, Sankhya, and J. Indian Statistical Association.
Currently he is a Coordinating Editor of the Journal of
Statistical Planning and Inference, and an Associate Editor of
Statistics and Probability Letters.
Contents: Introduction.- Asymptotic Properties of Weighted
Empirical Processes.- Linear Rank and Signed Rank Statistics.- M,
R and Some Scale Estimators.- Minimum Distance Estimators.-
Goodness-of-fit Tests in Regression.- Autoregression.- Nonlinear
Autoregression.
Series: Lecture Notes in Statistics. VOL. 166
2002. VIII, 474 pp. Softcover
3-540-43736-3
This book contains three state of the art texts on the following
subjects:
-Large Deviations and Iterating Random Walks
-Dawson-Watanabe Superprocesses and Measure-Valued Diffusions
-Semiparametric Statistics
Keywords: Probability Theory, semiparametric statistics, large
deviations, superprocesses . Mathematics Subject Classification (
2000 ): 60-01, 60-06, 60F05, 60F10, 60G57, 60J15, 60K35, 62-01,
62-06, 62F12, 62G05, 62G20, 82B24, 82B26
Contents: 1. Erwin Bolthausen: Large Deviations and Interacting
Random Walks.- 2. Edwin Perkins: Dawson-Dawson-Watanabe
Superprocesses and Measure-Valued Diffusions.- 3. A. W. van der
Vaart: Semiparametric Statistics.
Series: Lecture Notes in Mathematics. VOL. 1781
2002. VIII, 191 pp. Softcover
3-540-43726-6
Many problems of stability in the theory of dynamical systems
face the difficulty of small divisors. The most famous example is
probably given by Kolmogorov-Arnold-Moser theory in the context
of Hamiltonian systems, with many applications to physics and
astronomy. Other natural small divisor problems arise considering
circle diffeomorphisms or quasiperiodic Schroedinger operators.
In this volume Hakan Eliasson, Sergei Kuksin and Jean-Christophe
Yoccoz illustrate the most recent developments of this theory
both in finite and infinite dimension. A list of open problems (including
some problems contributed by John Mather and Michel Herman) has
been included.
Keywords: Dynamical systems, Small Divisors, Quasiperiodic (
orbits and flows ). Mathematics Subject Classification ( 2000 ):
37C55, 37F25, 37F50, 37J40, 37K55, 47B39, 34L40
Contents: Hakan Eliasson: Perturbations of Linear Quasi-Periodic
System.- Sergei B. Kuksin: KAM-Persistence of Finite-Gap
Solutions.- Jean-Christophe Yoccoz: Analytic Linearization of
Circle Diffeomorphisms.- Stefano Marmi and Jean-Christophe Yoccoz:
Some Open Problems Related to Small Divisors.
Series: Lecture Notes in Mathematics. VOL. 1784