9781138054882 - missing and modified data in nonparametric estimation: with r examples di efromovich, sam (19 risultati)
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Da: Basi6 International, Irving, TX, U.S.A.Basi6 International
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Hardcover. Condizione: new. Hardcover. This book presents a systematic and unified approach for modern nonparametric treatment of missing and modified data via examples of density and hazard rate estimation, nonparametric regression, filtering signals, and time series analysis. All basic types of missing at random and not at ran…dom, biasing, truncation, censoring, and measurement errors are discussed, and their treatment is explained. Ten chapters of the book cover basic cases of direct data, biased data, nondestructive and destructive missing, survival data modified by truncation and censoring, missing survival data, stationary and nonstationary time series and processes, and ill-posed modifications.The coverage is suitable for self-study or a one-semester course for graduate students with a prerequisite of a standard course in introductory probability. Exercises of various levels of difficulty will be helpful for the instructor and self-study.The book is primarily about practically important small samples. It explains when consistent estimation is possible, and why in some cases missing data should be ignored and why others must be considered. If missing or data modification makes consistent estimation impossible, then the author explains what type of action is needed to restore the lost information.The book contains more than a hundred figures with simulated data that explain virtually every setting, claim, and development. The companion R software package allows the reader to verify, reproduce and modify every simulation and used estimators. This makes the material fully transparent and allows one to study it interactively.Sam Efromovich is the Endowed Professor of Mathematical Sciences and the Head of the Actuarial Program at the University of Texas at Dallas. He is well known for his work on the theory and application of nonparametric curve estimation and is the author of Nonparametric Curve Estimation: Methods, Theory, and Applications. Professor Sam Efromovich is a Fellow of the Institute of Mathematical Statistics and the American Statistical Association. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Da: Ria Christie Collections, Uxbridge, Regno UnitoRia Christie Collections
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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Da: THE SAINT BOOKSTORE, Southport, Regno UnitoTHE SAINT BOOKSTORE
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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Hardcover. Condizione: Brand New. 443 pages. 10.50x7.25x1.00 inches. In Stock.
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Da: Mispah books, Redhill, SURRE, Regno UnitoMispah books
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Da: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
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Hardcover. Condizione: new. Hardcover. This book presents a systematic and unified approach for modern nonparametric treatment of missing and modified data via examples of density and hazard rate estimation, nonparametric regression, filtering signals, and time series analysis. All basic types of missing at random and not at ran…dom, biasing, truncation, censoring, and measurement errors are discussed, and their treatment is explained. Ten chapters of the book cover basic cases of direct data, biased data, nondestructive and destructive missing, survival data modified by truncation and censoring, missing survival data, stationary and nonstationary time series and processes, and ill-posed modifications.The coverage is suitable for self-study or a one-semester course for graduate students with a prerequisite of a standard course in introductory probability. Exercises of various levels of difficulty will be helpful for the instructor and self-study.The book is primarily about practically important small samples. It explains when consistent estimation is possible, and why in some cases missing data should be ignored and why others must be considered. If missing or data modification makes consistent estimation impossible, then the author explains what type of action is needed to restore the lost information.The book contains more than a hundred figures with simulated data that explain virtually every setting, claim, and development. The companion R software package allows the reader to verify, reproduce and modify every simulation and used estimators. This makes the material fully transparent and allows one to study it interactively.Sam Efromovich is the Endowed Professor of Mathematical Sciences and the Head of the Actuarial Program at the University of Texas at Dallas. He is well known for his work on the theory and application of nonparametric curve estimation and is the author of Nonparametric Curve Estimation: Methods, Theory, and Applications. Professor Sam Efromovich is a Fellow of the Institute of Mathematical Statistics and the American Statistical Association. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Da: THE SAINT BOOKSTORE, Southport, Regno UnitoTHE SAINT BOOKSTORE
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EUR 142,01
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Hardback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
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Da: moluna, Greven, Germaniamoluna
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book presents a systematic and unified approach for modern nonparametric treatment of missing and modified data via examples of density and hazard rate estimation, nonparametric regression, filtering signals, an…d time series analysis. All basic type.
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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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EUR 178,02
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Hardcover. Condizione: Brand New. 443 pages. 10.50x7.25x1.00 inches. In Stock. This item is printed on demand.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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EUR 189,73
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Buch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The book gives a unified approach to nonparametric curve estimation based on missing and modified data. Missing data includes cases of missing at random and missing not at random, while data modification includes truncation and censoring, typi…cal in survival analysis, as well as measurement errors and amplitude modulation. A universal nonparametric series E-estimator is used whose statistical idea is based on estimation of a population mean by a corresponding sample mean. While the approach is straightforward, the asymptotic theory shows that no other estimator can outperform the E-estimator.
