Bücher der Reihe Lecture Notes in Statistics

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.

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.

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.

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.

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.

Ever since the appearance of the book by Chhikara and Folks, a considerable number of publications in both theory and applications of the inverse Gaussian law have emerged thereby justifying the need for a comprehensive treatment of the issues involved.

This monograph contains a comprehensive account of the recent work of the authors and other workers on large sample optimal inference for non-ergodic models. The non-ergodic family of models can be viewed as an extension of the usual Fisher-Rao model for asymptotics, referred to here as an ergodic family.

The most important statistical concepts are the general linear model for Gaussian variables and the general methods of maximum likelihood estimation as well as the likelihood ratio test.

The theory of time series models has been well developed over the last thirt,y years. The most interesting feature of such a model is that its second order covariance analysis is ve~ similar to that for a linear model. This demonstrates the importance of higher order covariance analysis for nonlinear models.

From the reviews: "In this Lecture Note volume the author describes his differential-geometric approach to parametrical statistical problems summarizing the results he had published in a series of papers in the last five years.

An International Statistical Conference on Linear Inference was held in Poznan, Poland, on June 4-8, 1984. If the conference was really a success, it was due to all its participants who in various ways were devoting their time and efforts to make the conference fruitful and enjoyable.

This text evolved during a set of lectures given by the author at the Division of Statistics at the University of California, Davis in Fall 1986 and is based on the author's Habilitationsschrift submitted to the University of Marburg in Spring 1985 as well as on published and unpublished work.

Much of the traditional approach to linear model analysis is bound up in complex matrix expressions revolving about the usual generalized inverse. Initially this research was begun by Francis Hsuan and Pat Langenberg, without knowledge of Kruskal's paper published in 1975.

A few preliminaries 2 1. Likelihood and auxiliary statistics 1. Likelihood 4 1. Moments and cumulants of log likelihood derivatives 10 1. Marginal and conditional likelihood 15 * 1. Combinants, auxiliaries, and the p -model 19 1. Pseudo likelihood, profile likelihood and modified 30 profile likelihood 1.

The urgent need to describe and to solve certain problems connected to extreme phenomena in various areas of applications has been of decisive influence on the vital development of extreme value theory.