Parametric and Nonparametric Inference from Record-Breaking Data / Edition 1

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Overview

This book provides a comprehensive look at statistical inference from record-breaking data in both parametric and nonparametric settings, including Bayesian inference. A unique feature is that it treats the area of nonparametric function estimation from such data in detail, gathering results on this topic to date in one accessible volume. Previous books on records have focused mainly on the probabilistic behavior of records, prediction of future records, and characterizations of the distributions of record values, addressing some inference methods only briefly. The main purpose of this book is to fill this void on general inference from record values. Statisticians, mathematicians, and engineers will find the book useful as a research reference and in learning about making inferences from record-breaking data. The book can also serve as part of a graduate-level statistics or mathematics course, complementing material on the probabilistic aspects of record values. For a basic understanding of the statistical concepts, a one-year graduate course in mathematical statistics provides sufficient background. For a detailed understanding of the convergence theory of the nonparametric function estimators, a course in measure theory or probability theory at the graduate level is useful. Sneh Gulati is Associate Professor of Statistics at Florida International University in Miami. She is currently an associate editor of the Journal of Statistical Computation and Simulation and has published several articles in statistics. Currently she serves as the president of the South Florida Chapter of the American Statistical Association and is also the chair of the Florida Commission of Hurricane Loss Projection Methodology. William J. Padgett is Professor of Statistics and was the founding Chair of the Department of Statistics at the University of South Carolina, Columbia. He has published numerous papers and articles, as well as three books, on sta

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Editorial Reviews

From the Publisher
New record values in sports, finances, climate, ... are of interest to most people, and for about half a century, probabilists and statisticians have taken up the challenge of modelling their behaviour. The present monograph provides results on statistical inference problems for record-breaking data. For example: how to fit a parametric or nonparametric model to such data? Or also: how to predict the next record, based on the values of the past records. The main body of the book (Chapters 4-7) is a discussion of all the known work on nonparametric inference for this type of data.

The book will be a useful reference for researchers in this area. There could also be interest from engineers working in destructive stress testing and quality control.

ISI Short Book Reviews, Vol. 23/2, August 2003

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Product Details

  • ISBN-13: 9780387001388
  • Publisher: Springer New York
  • Publication date: 1/27/2003
  • Series: Lecture Notes in Statistics Series, #172
  • Edition description: 2003
  • Edition number: 1
  • Pages: 117
  • Product dimensions: 0.28 (w) x 6.14 (h) x 9.21 (d)

Table of Contents

1. Introduction.- 2. Preliminaries and Early Work.- 3. Parametric Inference.- 4. Nonparametric Inference—Genesis.- 5. Smooth Function Estimation.- 6. Bayesian Models.- 7. Record Models with Trend.- References.

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