Parametric and Nonparametric Inference from Record-Breaking Data

Analyzing data in the form of records is of particular important to statiticians working with financial and environmental data. This book summarizes the literature, and presents new results about how future performance can be predicted from past values.

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

Inhalt

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