Bücher der Reihe Lecture Notes in Statistics

In June 2010, a conference, Probability Approximations and Beyond, was held at the National University of Singapore (NUS), in honor of pioneering mathematician Louis Chen.

This is done by the use of two different estimation techniques, the pseudo maximum likelihood (PML) method and the generalized method of moments (GMM).The author details the statistical foundation of the GEE approach using more general estimation techniques.

The first two chapters are devoted to the basic theory of nonlinear functions of stationary Gaussian processes, Hermite polynomials, cumulants and higher order spectra, multiple Wiener-Ito integrals and finally chaotic Wiener-Ito spectral representation of subordinated processes.

There is always some uncertainty in our knowledge of both the initial conditions and the values of the physical constants that characterize the evolution of a physical system.

The symposium aimed to provide a review of the state of the art, define outstanding problems for research by theoreticians, transmit to practitioners recently developed algorithms, and stimulate interaction between statisticians and researchers in subject matter fields.

This book covers a wide range of topics in both discrete and continuous optimal designs. The topics discussed include designs for regression models, covariates models, models with trend effects, and models with competition effects.

Since 1972 the Institute of Mathematics and the Committee of Mathematics of the Polish Academy of Sciences organize annually con ferences on mathematical statistics in Wisla.

A title that arises from the International Spring School "Advances and Challenges in Space-Time modelling of Natural Events," which took place March 2010. It details developments, methods and applications in spatial statistics and related areas.

The product of a high-flying summer school in Paris in 2009, this volume synthesises the state of the art on ill-posed statistical inverse problems and high-dimensional estimation and explores the ways these techniques can be applied to economics.

Stochastic Orders in Reliability and Risk Management is composed of 19 contributions on the theory of stochastic orders, stochastic comparison of order statistics, stochastic orders in reliability and risk analysis, and applications.

These nine papers cover three different areas for longitudinal data analysis, four dealing with longitudinal data subject to measurement errors, four on incomplete longitudinal data analysis, and the last one for inferences for longitudinal data subject to outliers.

The First Seattle Symposium in Biostatistics: Survival Analysis was held on November 20and 21, 1995in honor ofthe twenty-fifth anniversary ofthe University of Washington (UW) School of Public Health and Community Medicine. The event was a big success. Exactly 5 years later, the Second Seattle Symposium in Biostatistics: Analysis of Correlated Data washeld on November 20 and 21, 2000, and it was also very successful. The event was sponsored by Pfizer and co-sponsored by the UW School of Public Health and Community Medicine and the Division of Public Health Sciences, the Fred Hutchinson Cancer Research Center (FHCRC). The symposium fea­ tured keynote lectures by Norman Breslow, David Cox and Ross Prentice, as well as invited talks by Raymond Carroll, Peter Diggle, Susan Ellen­ berg, Ziding Fan, Mitchell Gail, Stephen Lagakos, Nan Laird, Kung-Yee Liang, Roderick Little, Thoms Louis, David Oakes, Robert O'Neill, James Robins, Bruce Thrnbull, Mei-Cheng Wang and Jon Wellner. There were 336 attendees. In addition, 100 people attended the short course Analy­ sis of Longitudinal Data taught by Patrick Heagerty and Scott Zeger on November 18, and 96 attended the short course Analysis of Multivariate Failure Time Data taught by Danyu Lin, Lee-Jen Wei and Zhiliang Ying on November 19. When the UW School of Public Health and Community Medicine was formed in 1970, biostatistics as a discipline was only a few years old. In the subsequent thirty years, both the field and the UW Department of Biostatistics have evolved in many exciting ways.

This book develops Doukhan/Louhichi's 1999 idea to measure asymptotic independence of a random process. The authors, who helped develop this theory, propose examples of models fitting such conditions: stable Markov chains, dynamical systems or more complicated models, nonlinear, non-Markovian, and heteroskedastic models with infinite memory.

GM always considered that the distinction between the theory and practice was purely academic.When GM tackled practical problems, he used his skill as a physicist to extract the salient features and to select variables which could be measured meaningfully and whose values could be estimated from the available data.

Engle showed that this model, which he called ARCH (autoregressive conditionally heteroscedastic), is well-suited for the description of economic and financial price. This monograph concentrates on mathematical statistical problems associated with fitting conditionally heteroscedastic time series models to data.

Deconvolution problems occur in many ?elds of nonparametric statistics, for example, density estimation based on contaminated data, nonparametric - gression with errors-in-variables, image and signal deblurring. this means producing an empirical version h of h and, then, applying a deconvolution procedure to h to estimate f.

This refereed volume includes papers presented at a conference on modelling longitudinal and spatially correlated data. Many of the best researchers in the world have presented papers in an area with important applications to biostatistics and the environmental sciences.

The most widely used statistical method in seasonal adjustment is implemented in the X-11 Variant of the Census Method II Seasonal Adjustment Program. While these integrate parametric methods, they remain close to the initial X-11 method, and it is this "core" that Seasonal Adjustment with the X-11 Method focuses on.

The book covers the basic theory of linear regression models and presents a comprehensive survey of different estimation techniques as alternatives and complements to least squares estimation. The book is rounded off by an introduction to the basics of decision theory and an appendix on matrix algebra.

The identification of important risk factors and the quantification of risk stemming from an interplay between many risk factors is a prerequisite for mastering the challenges of risk perception, analysis and management successfully.

The book focuses on stochastic modeling of population processes. The book presents new symbolic mathematical software to develop practical methodological tools for stochastic population modeling. The book assumes calculus and some knowledge of mathematical modeling, including the use of differential equations and matrix algebra.

The development of Markov Chain in Monte Carlo Methods allow Bayesian statisticians to perform computations that were impossible just a few years ago. This book is of interest to researchers in this active area.

Wavelet methods have become a widely spread tool in signal and image process ing tasks. On the other hand, the presented material does cover a whole range of methodologies, and in that sense, the book may serve as an introduction into the domain of wavelet smoothing.

Statistical disclosure control is the discipline that deals with producing statistical data that are safe enough to be released to external researchers.

Despite its short history, wavelet theory has found applications in a remarkable diversity of disciplines: mathematics, physics, numerical analysis, signal processing, probability theory and statistics.

The aim of this book is to discuss various aspects associated with disseminating personal or business data collected in censuses or surveys or copied from administrative sources.

This book satisfies this need by a presentation of the spatstat package and many statistical examples.Researchers, spatial statisticians and scientists from biology, geosciences, materials sciences and other fields will use this book as a helpful guide to the application of point process statistics.

This book gives an account of recent developments in the field of probability and statistics for dependent data.