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Parametric and Nonparametric Inference from Record-Breaking Data (Lecture Notes in Statistics, 172) - Softcover
Synopsis
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.
"synopsis" may belong to another edition of this title.
Review
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
"About this title" may belong to another edition of this title.
- PublisherSpringer
- Publication date2003
- ISBN 10 0387001387
- ISBN 13 9780387001388
- BindingPaperback
- LanguageEnglish
- Number of pages125
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Parametric and Nonparametric Inference from Record-Breaking Data (Lecture Notes in Statistics)
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Paperback. Condition: Good. Volume 172 in series. No DJ. Ex University of California, Berkeley Math/Stat Library book with usual library markings. Binding is tight, text clean. No other marks in book. Appears unread. Softcover overbound with clear plastic hardcover. Seller Inventory # mon0000017869
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Parametric and Nonparametric Inference from Record-Breaking Data (Lecture Notes in Statistics)
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Paperback. Condition: Fine. Unread. A comprehensive look at statistical inference from record-breaking data in both parametric and nonparametric settings, including Bayesian inference. Chapters (7): Introduction; Preliminaries & Early Work; Parametric Inference; Nonparametric Inference-Genesis; Smooth Function Estimation; Bayesian Models; Record Models with Trend. Seller Inventory # 004072
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Parametric and Nonparametric Inference from Record-Breaking Data.
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XI, 113 p. Softcover Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Lecture Notes in Statistics, 172. Sprache: Englisch. Seller Inventory # 36898HB
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Parametric and Nonparametric Inference from Record-breaking Data.
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New York, NY, U.S.A. Springer-Verlag New York, Incorporated, 2003
ISBN 10: 0387001387
ISBN 13: 9780387001388
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Softcover
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Softcover. Condition: Gut. Gebraucht - Gut Zustand: Gut, Mängelexemplar, VIII, 113 p. 1 illus. About this book: 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 graduatelevel 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 Written for researchers None. Seller Inventory # 14032
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Parametric and Nonparametric Inference from Record-Breaking Data (Lecture Notes in Statistics, 172)
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