Bøger i Lecture Notes in Statistics serien

Incomplete-data problems arise naturally in many instances of statistical practice. One main problem still to be resolved is the development of appropriate inference methodology from merged files if one insists on using file merging methodology.

Balakrishnan Hamilton, Ontario Canada November, 1988 Table of Contents Chapter 1: TIlE DISTRIBUTION OF ORDER STATISTICS Exercises 4 Chapter 2: RECURRENCE RELATIONS AND IDENTITIES FOR ORDER STATISTICS 2. Results for two related populations 2. Results for exchangeable variates 23 25 Exercises Chapter 3: BOUNDS ON EXPECTATIONS OF ORDER STATISTICS 3.

Besides their previous statistics courses, these students have had an introductory course in computer programming (FORTRAN, Pascal, or C) and courses in calculus and linear algebra, so that they may not be typical students of sociology.

Given a series of wind speeds and directions from the port of Fremantle the aim of this monograph is to detect general weather patterns and seasonal characteristics.

Search theory is concerned with the location of a 'target' given imprecise information concerning its location. This volume is concerned with search strategies which are optimal in the sense that they minimize the 'risk' or cost of a search where this may be measured in factors such as time or money.

In June of 1990, a conference was held on Probablity Models and Statisti cal Analyses for Ranking Data, under the joint auspices of the American Mathematical Society, the Institute for Mathematical Statistics, and the Society of Industrial and Applied Mathematicians.

This is the first book on the topic of probability matching priors. It targets researchers, Bayesian and frequentist; graduate students in Statistics.

There exists a large variety of image reconstruction methods proposed by different authors (see e. We assume that the image belongs to a certain functional class and we find the image estimators that achieve the best order of accuracy for the worst images in the class.

The past few years have witnessed dramatic advances in computational methods for Bayesian inference. As a result, Bayesian approaches to solving a wide variety of problems in data analysis and decision-making have become feasible, and there is currently a growth spurt in the application of Bayesian methods.

Mixing is concerned with the analysis of dependence between sigma-fields defined on the same underlying probability space. The second part describes mixing properties of classical processes and random fields as well as providing a detailed study of linear and Gaussian fields.

In the present work I want to show a mathematical study of the statistical notion of sufficiency mainly for undominated statistical experiments. Though it is more restrictive than Le Cam's paper(1964), a study which is connected more directly with the classical papers of Halmos and Savage(1949) , and Bahadur(1954) is shown.

The author reviews the essentials of logistic regression and discusses the variety of mechanisms which might cause missing values while the rest of the book covers the methods which may be used to deal with missing values and their effectiveness.

Voronoi tessellations are produced by first considering a set of points (known as nuclei) in d-space, and then defining cells as the set of points which are closest to each nuclei. A random Voronoi tessellation is produced by supposing that the location of each nuclei is determined by some random process.

This volume is a selection of papers presented at the Fourth International Workshop on Artificial Intelligence and Statistics held in January 1993. In particular, there is agreement that the fundamental problem is the avoidence of "overfitting"-Le., where a model fits the given data very closely, but is a poor predictor for new data;

In many applied fields of statistics the concept of causality is central to a scientific investigation. The author's aim in this book is to extend the classical theories of probabilistic causality to longitudinal settings and to propose that interesting causal questions can be related to causal effects which can change in time.

Classifying objects according to their likeness seems to have been a step in the human process of acquiring knowledge, and it is certainly a basic part of many of the sciences. Thus, classification is close to factorial analysis methods and to multi-dimensional scaling methods.

The present monograph is a comprehensive summary of the research on visibility in random fields, which I have conducted with the late Professor Micha Yadin for over ten years.

This textbook has been designed as an introduction to stochastic orderings and dependence, and their applications to queues and the networks of queues. It is assumed that readers have a firm grounding in Lebesgue measure, conditional expectation and martingales.

Like its predecessor, this second volume presents detailed applications of Bayesian statistical analysis, each of which emphasizes the scientific context of the problems it attempts to solve. The emphasis of this volume is on biomedical applications. These papers were presented at a workshop at Carnegie-Mellon University in 1993.

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.

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).

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.

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.

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.

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.

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.

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.

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.