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The 5th Workshop on Case Studies in Bayesian Statistics was held at the Carnegie Mellon University campus on September 24-25, 1999. INVITED PAPERS The three invited cases studies at the workshop discuss problems in ed ucational policy, clinical trials design, and environmental epidemiology, respectively.

This book outlines and demonstrates problems with the use of the HP filter, and proposes an alternative strategy for inferring cyclical behavior from a time series featuring seasonal, trend, cyclical and noise components.

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

This account of recent works on weakly dependent, long memory and multifractal processes introduces new dependence measures for studying complex stochastic systems and includes other topics such as the dependence structure of max-stable processes.

This book presents a unified approach for obtaining the limiting distributions of minimum distance. It 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.

This concise, easy-to-follow book stimulates interest and develops proficiency in statistical analysis.

This volume is a collection of survey papers on recent developments in the fields of quasi-Monte Carlo methods and uniform random number generation. Hence, both in uniform random number generation and in quasi-Monte Carlo methods, we study the uniformity of deterministically generated point sets in high dimensions.

This book will be of interest to mathematical statisticians and biometricians interested in block designs. After presenting the general theory of analysis based on the randomization model in Part I, the constructional and combinatorial properties of design are described in Part II.

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.

Assume we are interested in approximating the distribution of a statistical functional T(P ) the -1 nn empirical counterpart of the functional T(P) , where P n := n l:i=l aX. , Xm n n -1 mn * * P T(P ) conditionally on := mn l: i =1 a * ' then the behaviour of P m n,m n n n X.

The aim of this monograph is to show how random sums (that is, the summation of a random number of dependent random variables) may be used to analyse the behaviour of branching stochastic processes. The author shows how these techniques may yield insight and new results when applied to a wide range of branching processes.

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.

The purpose of this book is to discuss whether statistical methods make sense. We have made the link with one widely accepted view of science and we have explained the senses in which Bayesian statistics and p-values allow us to draw conclusions.

This book gives a comprehensive introduction to exponential family nonlinear models, which are the natural extension of generalized linear models and normal nonlinear regression models. The differential geometric framework is presented for these models and the geometric methods are widely used in this book.

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

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.

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.

Permutation testing for multivariate stochastic ordering and ANOVA designs is a fundamental issue in many scientific fields such as medicine, biology, pharmaceutical studies, engineering, economics, psychology, and social sciences. This book presents new advanced methods and related R codes to perform complex multivariate analyses.

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 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 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.

Their niche of mathematics is the abstract pattern of reproduction, sets of individuals changing size and composition through their members reproducing; Branching is a clean and beautiful mathematical pattern, with an intellectually challenging intrinsic structure, and it pervades the phenomena it underlies.

Offers a fresh, fairly efficient, and robust alternative to analyzing multivariate data. This monograph provides an overview of the theory of multivariate nonparametric methods based on spatial signs and ranks. It uses marginal signs and ranks and different type of L1 norm.

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.

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.

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.

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.

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.

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.

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.