Series: Dynamic Modeling and Econometrics in Economics and
Finance, Vol. 9
2005, Approx. 350 p., Hardcover
ISBN: 0-387-25760-8
About this book
The basic characteristic of Modern Linear and Nonlinear
Econometrics is that it presents a unified approach of modern
linear and nonlinear econometrics in a concise and intuitive way.
It covers four major parts of modern econometrics: linear and
nonlinear estimation and testing, time series analysis, models
with categorical and limited dependent variables, and, finally, a
thorough analysis of linear and nonlinear panel data modeling.
Distinctive features of this handbook are:
-A unified approach of both linear and nonlinear econometrics,
with an integration of the theory and the practice in modern
econometrics. Emphasis on sound theoretical and empirical
relevance and intuition. Focus on econometric and statistical
methods for the analysis of linear and nonlinear processes in
economics and finance, including computational methods and
numerical tools.
-Completely worked out empirical illustrations are provided
throughout, the macroeconomic and microeconomic (household and
firm level) data sets of which are available from the internet;
these empirical illustrations are taken from finance (e.g. CAPM
and derivatives), international economics (e.g. exchange rates),
innovation economics (e.g. patenting), business cycle analysis,
monetary economics, housing economics, labor and educational
economics (e.g. demand for teachers according to gender) and many
others.
-Exercises are added to the chapters, with a focus on the
interpretation of results; several of these exercises involve the
use of actual data that are typical for current empirical work
and that are made available on the internet.
What is also distinguishable in Modern Linear and Nonlinear
Econometrics is that every major topic has a number of examples,
exercises or case studies. By this `learning by doing' method the
intention is to prepare the reader to be able to design, develop
and successfully finish his or her own research and/or solve real
world problems.
Table of contents
Acknowledgements.- Part I. Linear and Nonlinear Econometric
Inference: Estimation and Testing. Estimation in Linear and
Nonlinear Models. Generalized Methods of Moments. Testing in
Linear and Nonlinear Models.- Part II. Time Series Analysis. A
Typology of Dynamic Models. Univariate ARIMA Models.
Cointegration and Transfer Functions. Multivariate Time Series.
Varying Parameters Models.- Part III. Categorical and Limited
Dependent Variables. Discrete Choice Models. Limited responses,
duration and count data.- Part IV. Panel Data Analysis. Linear
Panel Data Models. Nonlinear Panel Data Models.- A. Nonlinear
Optimization and Estimation.- B. Mathematical Formulation of GMM.-
C. Stability Criteria for AR(p) Models.- D. MLE of the RSM with
Endogenous Prices.- E. Volatility Modeling.
Series: Statistics for Social Science and Behavorial Sciences
2005, XXIV, 416 p. 44 illus., Hardcover
ISBN: 0-387-20272-2
About this book
This book begins with a reflection on the history of test design--the
core activity of all educational and psychological testing. It
then presents a standard language for modeling test design
problems as instances of multi-objective constrained optimization.
The main portion of the book discusses test design models for a
large variety of problems from the daily practice of testing, and
illustrates their use with the help of numerous empirical
examples. The presentation includes models for the assembly of
tests to an absolute or relative target for their information
functions, classical test assembly, test equating problems, item
matching, test splitting, simultaneous assembly of multiple
tests, tests with item sets, multidimensional tests, and adaptive
test assembly. Two separate chapters are devoted to the questions
of how to design item banks for optimal support of programs with
fixed and adaptive tests. Linear Models for Optimal Test Design,
which does not require any specific mathematical background, has
been written to be a helpful resource on the desk of any test
specialist.
Table of contents
2005, XVIII, 342 p. 149 illus., Hardcover
ISBN: 0-387-25464-1
About this textbook
A Classical Introduction to Cryptography: Applications for
Communications Security introduces fundamentals of information
and communication security by providing appropriate mathematical
concepts to prove or break the security of cryptographic schemes.
This advanced-level textbook covers conventional cryptographic
primitives and cryptanalysis of these primitives; basic algebra
and number theory for cryptologists; public key cryptography and
cryptanalysis of these schemes; and other cryptographic
protocols, e.g. secret sharing, zero-knowledge proofs and
undeniable signature schemes.
A Classical Introduction to Cryptography: Applications for
Communications Security is rich with algorithms, including
exhaustive search with time/memory tradeoffs; proofs, such as
security proofs for DSA-like signature schemes; and classical
attacks such as collision attacks on MD4. Hard-to-find standards,
e.g. SSH2 and security in Bluetooth, are also included.
Table of contents
Preface.- Prehistory of Cryptography.- Conventional Cryptography.-
Dedicated Conventional Cryptographic Primitives.- Conventional
Security Analysis.- Security Protocols with Conventional
Cryptography.- Algorithmic Algebra.- Algorithmic Number Theory.-
Elements of Complexity Theory.- Public Key Cryptography.- Digital
Signatures.- Cryptographic Protocols.- From Cryptography to
Communication Security.- Bibliography.- Index.
Series: Lecture Notes in Statistics, Vol. 182
2005, XII, 188 p. 17 illus., Softcover
ISBN: 0-387-25038-7
About this book
A fundamental issue in statistical analysis is testing the fit of
a particular probability model to a set of observed data. Monte
Carlo approximation to the null distribution of the test provides
a convenient and powerful means of testing model fit.
Nonparametric Monte Carlo Tests and Their Applications proposes a
new Monte Carlo-based methodology to construct this type of
approximation when the model is semistructured. When there are no
nuisance parameters to be estimated, the nonparametric Monte
Carlo test can exactly maintain the significance level, and when
nuisance parameters exist, this method can allow the test to
asymptotically maintain the level.
The author addresses both applied and theoretical aspects of
nonparametric Monte Carlo tests. The new methodology has been
used for model checking in many fields of statistics, such as
multivariate distribution theory, parametric and semiparametric
regression models, multivariate regression models, varying-coefficient
models with longitudinal data, heteroscedasticity, and
homogeneity of covariance matrices. This book will be of interest
to both practitioners and researchers investigating goodness-of-fit
tests and resampling approximations.
Every chapter of the book includes algorithms, simulations, and
theoretical deductions. The prerequisites for a full appreciation
of the book are a modest knowledge of mathematical statistics and
limit theorems in probability/empirical process theory. The less
mathematically sophisticated reader will find Chapters 1, 2 and 6
to be a comprehensible introduction on how and where the new
method can apply and the rest of the book to be a valuable
reference for Monte Carlo test approximation and goodness-of-fit
tests.
Lixing Zhu is Associate Professor of Statistics at the University
of Hong Kong. He is a winner of the Humboldt Research Award at
Alexander-von Humboldt Foundation of Germany and an elected
Fellow of the Institute of Mathematical Statistics.
Table of contents
Monte Carlo Tests.- Testing for Multivariate Distributions.-
Asymptotics of Goodness-of-fit Tests for Symmetry.- A Test of
Dimension-reduction Type for Regressios.- Checking the Adequacy
of a Partially Linear Model.- Model Checking for Multivariate
Regression Models.- Heteroscedasticity Tests for Regressions.-
Checking the Adequacy of a Varying-Coefficients Model in
Longitudinal Studies.- On the Mean Residual Life Regression Model.-
Homogeneity Testing for Covariance Matrices.
Series: Grundlehren der mathematischen Wissenschaften, Vol.
332
2005, Approx. 500 p., Hardcover
ISBN: 3-540-27949-0
About this textbook
Categories and sheaves, which emerged in the middle of the last
century as an enrichment for the concepts of sets and functions,
appear almost everywhere in mathematics nowadays.
This book covers categories, homological algebra and sheaves in a
systematic and exhaustive manner starting from scratch, and
continues with full proofs to an exposition of the most recent
results in the literature, and sometimes beyond.
The authors present the general theory of categories and
functors, emphasising inductive and projective limits, tensor
categories, representable functors, ind-objects and localization.
Then they study homological algebra including additive, abelian,
triangulated categories and also unbounded derived categories
using transfinite induction and accessible objects. Finally,
sheaf theory as well as twisted sheaves and stacks appear in the
framework of Grothendieck topologies.
Table of contents
The language of categories.- Limits.- Filtrant Limits.- Tensor
categories.- Generators and Representability.- Indization of
categories.- Localization.- Additive and Abelian categories.- pi-accessible
objects and F-injective Objects.- Triagulated categories.-
Complexes in additive categories.- Complexes in Abelian
Categories.- Derived Categories.- Unbounded Derived Categories.-
Indization and Derivation of Abelian Categories.- Grothendieck
Topologies.- Sheaves on Grothendieck Topologies.- Abelian Sheaves.-
Stacks and Twisted Sheaves.- References.- Notations.
Series: Springer Texts in Statistics
2006, Approx. 300 p., Hardcover
ISBN: 0-387-20276-5
About this book
This textbook is suitable for a graaduate-level course for
students interested in nonacademic jobs, such as for a
pharmaceutical company. The needs of these students differ from
those to plan to do statistical research, and the emphasis is on
topics of applied interest.
Table of contents
The Role of Models in Statistical Inference * Likelihood
Construction and Methods * Large Sample Theory: The Basics *
Consistency and Asymptotic Normality of Maximum Likelihood
Estimators * M-Estimation (Asymptotic Results for Extimating
Equations) * Monte Carlo Simulation Studies * Nonparametric
Methods for Obtaining Standard Errors * Bootstrap Confidence
Intervals and Hypothesis Tests * Permutation Tests * Modern
Nonparametric Statistics