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  • Sprache: Englisch

    Verlag: Springer, 2022

    3031121031 / 9783031121036

    • Hardcover

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    EUR 140,97

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    Zustand: New. In English.

  • Sprache: Englisch

    Verlag: Springer, 2023

    3031121066 / 9783031121067

    • Softcover

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  • Sprache: Englisch

    Verlag: Springer, 2022

    3031121031 / 9783031121036

    • Hardcover

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    Zustand: Hervorragend. Zustand: Hervorragend | Seiten: 288 | Sprache: Englisch | Produktart: Bücher | This book emphasizes the use of stochastic orders as motivational tools for developing new statistical procedures. Stochastic orders have found useful applications in many disciplines, including reliability theory, survival analysis, risk theory, finance, nonparametric methods, economics and actuarial science. Written by a statistician, this volume clarifies the connection between stochastic orders and nonparametric methods.The importance of order statistics and spacings is well recognized. Classically, they mainly focus on the case when the observations are independent and identically distributed, however, several new developments have extended the comparison of order statistics to the case of non-identically distributed or non-independent observations. In addition to giving a detailed discussion of various topics in the general area of stochastic orders, a substantial part of the book is devoted to recent research on stochastic comparisons of order statistics andspacings, including a long chapter on dependence among them. The book will be useful for graduate students and researchers in statistics, economics, actuarial science and other related disciplines. In particular, with close to 300 references, it will be a valuable resource for reliability theorists, applied probabilists and statisticians. Readers are expected to have taken a first-year graduate level course in mathematical statistics or in applied probability.

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    Sprache: Englisch

    Verlag: Springer, 2023

    3031121066 / 9783031121067

    • Softcover

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    Taschenbuch. Zustand: Neu. Stochastic Comparisons with Applications | In Order Statistics and Spacings | Subhash C. Kochar | Taschenbuch | xvi | Englisch | 2023 | Springer | EAN 9783031121067 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

  • Sprache: Englisch

    Verlag: Springer Nature, 2023

    3031121066 / 9783031121067

    • Softcover

    Anbieter: Revaluation Books, Exeter, Vereinigtes KönigreichRevaluation Books

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    Zustand: Neu

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    Paperback. Zustand: Brand New. 288 pages. 9.26x6.10x0.75 inches. In Stock.

  • Sprache: Englisch

    Verlag: Springer Nature, 2022

    3031121031 / 9783031121036

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    Hardcover. Zustand: Brand New. 288 pages. 9.25x6.10x0.69 inches. In Stock.

  • Sprache: Englisch

    Verlag: Springer, 2022

    3031121031 / 9783031121036

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  • Sprache: Englisch

    Verlag: Springer, 2023

    3031121066 / 9783031121067

    • Softcover

    Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH

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    Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book emphasizes the use of stochastic orders as motivational tools for developing new statistical procedures. Stochastic orders have found useful applications in many disciplines, including reliability theory, survival analysis, risk theory, finance, nonparametric methods, economics and actuarial science. Written by a statistician, this volume clarifies the connection between stochastic orders and nonparametric methods.The importance of order statistics and spacings is well recognized. Classically, they mainly focus on the case when the observations are independent and identically distributed, however, several new developments have extended the comparison of order statistics to the case of non-identically distributed or non-independent observations. In addition to giving a detailed discussion of various topics in the general area of stochastic orders, a substantial part of the book is devoted to recent research on stochastic comparisons of order statistics andspacings, including a long chapter on dependence among them. The book will be useful for graduate students and researchers in statistics, economics, actuarial science and other related disciplines. In particular, with close to 300 references, it will be a valuable resource for reliability theorists, applied probabilists and statisticians.Readers are expected to have taken a first-year graduate level course in mathematical statistics or in applied probability.

  • Sprache: Englisch

    Verlag: Springer, 2022

    3031121031 / 9783031121036

    • Hardcover

    Anbieter: AHA-BUCH GmbH, Einbeck, DeutschlandAHA-BUCH GmbH

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    Zustand: Neu

    EUR 210,11

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    Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book emphasizes the use of stochastic orders as motivational tools for developing new statistical procedures. Stochastic orders have found useful applications in many disciplines, including reliability theory, survival analysis, risk theory, finance, nonparametric methods, economics and actuarial science. Written by a statistician, this volume clarifies the connection between stochastic orders and nonparametric methods.The importance of order statistics and spacings is well recognized. Classically, they mainly focus on the case when the observations are independent and identically distributed, however, several new developments have extended the comparison of order statistics to the case of non-identically distributed or non-independent observations. In addition to giving a detailed discussion of various topics in the general area of stochastic orders, a substantial part of the book is devoted to recent research on stochastic comparisons of order statistics andspacings, including a long chapter on dependence among them. The book will be useful for graduate students and researchers in statistics, economics, actuarial science and other related disciplines. In particular, with close to 300 references, it will be a valuable resource for reliability theorists, applied probabilists and statisticians.Readers are expected to have taken a first-year graduate level course in mathematical statistics or in applied probability.