Bücher der Reihe Lecture Notes in Statistics

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.

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.

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.

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on the author's Habilitationsschrift submitted to the University of Marburg in Spring 1985 as well as on published and unpublished work.

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.

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.

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.

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.

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.

This book is intended for anyone, regardless of discipline, who is interested in the use of statistical methods to help obtain scientific explanations or to predict the outcomes of actions, experiments or policies.

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.

The need to understand and predict the processes that influence the Earth's atmosphere is one of the grand scientific challenges for the next century. This volume is a series of case studies and review chapters that cover many of the recent developments in statistical methodology that are useful for interpreting atmospheric data.

Senior probabilists from around the world with widely differing specialities gave their visions of the state of their specialty, why they think it is important, and how they think it will develop in the new millenium.

The classical approaches of experimental design and robust statistics are introduced before the areas are linked, and the author shows that robust statistical procedures profit by an appropriate choice of the design and that efficient designs for a robust statistical analysis are more applicable.

This book is a concise presentation of the normal distribution on the real line and its counterparts on more abstract spaces, which we shall call the Gaussian distributions.

This volume contains the edited proceedings of the First World Congress on Branching Processes. The contributions present new research and surveys of current research activity in this field.

Nevertheless, it was not until the early 1940's that such a statistical theory - namely Sequential Analysis - was created, which allows us to investigate this kind of behaviour in a precise manner.

The genesis of this volume was in a one-day meeting arranged under the auspices of the Nathematical Ecology Group, jointly of the British Region of the Biometric Society and the British Ecological Society, and held in the Natural History Museum in London on the 4th May 1982.

This volume contains invited case studies with the accompanying discussion as well as contributed papers selected by a refereeing process of 6th Workshop on Case Studies in Bayesian Statistics was held at the Carnegie Mellon University in October, 2001.

This book presents a method of establishing explicit solutions to classical problems of calculating the best lower and upper mean-variance bounds. The following families of distributions are taken into account: arbitrary, symmetric, symmetric unimodal, and U-shaped.

He also served as a faculty member in the University of Ife, Nigeria, Moi University, Kenya, University of South Pacific, Fiji Islands and held visiting positions at University of Montreal, University of Windsor, Stockholm University, University of Western Australia, etc.

The papers in this volume discuss important methodological advances in several important areas, including multivariate failure time data and interval censored data. The book will be an indispensable reference for researchers and practitioners in biostatistics, medical research, and the health sciences.

The papers were presented and discussed at a workshop held at Carnegie-Mellon University, and this volume - dedicated to the memory of Morrie Groot-reproduces six invited papers, each with accompanying invited discussion, and nine contributed papers with the focus on econometric applications.

Other papers discuss methods for finite parameter Gaussian models, time series with infinite variance or stable marginal distribution, frequency domain methods, long range dependent processes, nonstationary processes, and nonlinear time series.

Havenner begins by summarizing six reasons state space methods are advanta geous, and then walks the reader through construction and evaluation of a state space model for four monthly macroeconomic series: industrial production in dex, consumer price index, six month commercial paper rate, and money stock (Ml).

Ten years ago Bill Gale of AT&T Bell Laboratories was primary organizer of the first Workshop on Artificial Intelligence and Statistics. Thus, statisticians have traditionally spent considerably more time exploiting prior information of the environment to model data and exploratory data analysis methods tailored to their assumptions.

In order to obtain many of the classical results in the theory of statistical estimation, it is usual to impose regularity conditions on the distributions under consideration.