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© 2003

Parametric and Nonparametric Inference from Record-Breaking Data

Book

Part of the Lecture Notes in Statistics book series (LNS, volume 172)

Table of contents

  1. Front Matter
    Pages N2-viii
  2. Sneh Gulati, William J. Padgett
    Pages 1-4
  3. Sneh Gulati, William J. Padgett
    Pages 5-9
  4. Sneh Gulati, William J. Padgett
    Pages 11-32
  5. Sneh Gulati, William J. Padgett
    Pages 33-44
  6. Sneh Gulati, William J. Padgett
    Pages 45-65
  7. Sneh Gulati, William J. Padgett
    Pages 67-80
  8. Sneh Gulati, William J. Padgett
    Pages 81-104
  9. Back Matter
    Pages 105-117

About this book

Introduction

As statisticians, we are constantly trying to make inferences about the underlying population from which data are observed. This includes estimation and prediction about the underlying population parameters from both complete and incomplete data. Recently, methodology for estimation and prediction from incomplete data has been found useful for what is known as "record-breaking data," that is, data generated from setting new records. There has long been a keen interest in observing all kinds of records-in particular, sports records, financial records, flood records, and daily temperature records, to mention a few. The well-known Guinness Book of World Records is full of this kind of record information. As usual, beyond the general interest in knowing the last or current record value, the statistical problem of prediction of the next record based on past records has also been an important area of record research. Probabilistic and statistical models to describe behavior and make predictions from record-breaking data have been developed only within the last fifty or so years, with a relatively large amount of literature appearing on the subject in the last couple of decades. This book, written from a statistician's perspective, is not a compilation of "records," rather, it deals with the statistical issues of inference from a type of incomplete data, record-breaking data, observed as successive record values (maxima or minima) arising from a phenomenon or situation under study. Prediction is just one aspect of statistical inference based on observed record values.

Keywords

Estimator data analysis mathematical statistics probability theory statistical inference statistics

Authors and affiliations

  1. 1.Department of StatisticsFlorida International UniversityMiamiUSA
  2. 2.Department of StatisticsUniversity of South CarolinaColumbiaUSA

Bibliographic information

  • Book Title Parametric and Nonparametric Inference from Record-Breaking Data
  • Authors Sneh Gulati
    William J. Padgett
  • Series Title Lecture Notes in Statistics
  • DOI https://doi.org/10.1007/978-0-387-21549-5
  • Copyright Information Springer-Verlag New York 2003
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Softcover ISBN 978-0-387-00138-8
  • eBook ISBN 978-0-387-21549-5
  • Series ISSN 0930-0325
  • Edition Number 1
  • Number of Pages VIII, 117
  • Number of Illustrations 2 b/w illustrations, 0 illustrations in colour
  • Topics Statistical Theory and Methods
  • Buy this book on publisher's site
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Reviews

 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