Sneh Gulati, William J. Padgett

Parametric and Nonparametric Inference from Record-Breaking Data (eBook, PDF)

• Format: PDF

Produktbeschreibung

By providing a comprehensive look at statistical inference from record-breaking data in both parametric and nonparametric settings, this book treats the area of nonparametric function estimation from such data in detail.

Its main purpose is to fill this void on general inference from record values.

Statisticians, mathematicians, and engineers will find the book useful as a research reference. It can also serve as part of a graduate-level statistics or mathematics course.

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Produktdetails

• Verlag: Springer New York
• Seitenzahl: 117
• Erscheinungstermin: 14. März 2013
• Englisch
• ISBN-13: 9780387215495
• Artikelnr.: 43998575

Inhaltsangabe

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.

Rezensionen

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