Isbn: 9783031300752 - visualization and imputation of missing values: with applications in r (statistics and computing) (5 resultados)

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  • Idioma: Inglés

    Editorial: Springer, 2024

    3031300750 / 9783031300752

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    Librería: preigu, Osnabrück, Alemaniapreigu

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    EUR 149,10

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    Taschenbuch. Condición: Neu. Visualization and Imputation of Missing Values | With Applications in R | Matthias Templ | Taschenbuch | Statistics and Computing | xxii | Englisch | 2024 | Springer | EAN 9783031300752 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

  • Idioma: Inglés

    Editorial: Springer, 2024

    3031300750 / 9783031300752

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    Librería: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand

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    Condición: new. Questo è un articolo print on demand.

  • Idioma: Inglés

    Editorial: Springer Verlag Gmbh Nov 2024, 2024

    3031300750 / 9783031300752

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    Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.

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    EUR 171,19

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    Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware Englisch.

  • Idioma: Inglés

    Editorial: Springer, Springer International Publishing Nov 2024, 2024

    3031300750 / 9783031300752

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    Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000

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    Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book explores visualization and imputation techniques for missing values and presents practical applications using the statistical software R. It explains the concepts of common imputation methods with a focus on visualization, description of data problems and practical solutions using R, including modern methods of robust imputation, imputation based on deep learning and imputation for complex data. By describing the advantages, disadvantages and pitfalls of each method, the book presents a clear picture of which imputation methods are applicable given a specific data set at hand.The material covered includes the pre-analysis of data, visualization of missing values in incomplete data, single and multiple imputation, deductive imputation and outlier replacement, model-based methods including methods based on robust estimates, non-linear methods such as tree-based and deep learning methods, imputation of compositional data, imputation quality evaluation from visual diagnostics to precision measures, coverage rates and prediction performance and a description of different model- and design-based simulation designs for the evaluation. The book also features a topic-focused introduction to R and R code is provided in each chapter to explain the practical application of the described methodology.Addressed to researchers, practitioners and students who work with incomplete data, the book offers an introduction to the subject as well as a discussion of recent developments in the field. It is suitable for beginners to the topic and advanced readers alike.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 484 pp. Englisch.

  • Idioma: Inglés

    Editorial: Palgrave Macmillan, 2024

    3031300750 / 9783031300752

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    Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH

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    EUR 239,31

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    Cantidad disponible: 1 disponibles

    Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book explores visualization and imputation techniques for missing values and presents practical applications using the statistical software R. It explains the concepts of common imputation methods with a focus on visualization, description of data problems and practical solutions using R, including modern methods of robust imputation, imputation based on deep learning and imputation for complex data. By describing the advantages, disadvantages and pitfalls of each method, the book presents a clear picture of which imputation methods are applicable given a specific data set at hand.The material covered includes the pre-analysis of data, visualization of missing values in incomplete data, single and multiple imputation, deductive imputation and outlier replacement, model-based methods including methods based on robust estimates, non-linear methods such as tree-based and deep learning methods, imputation of compositional data, imputation quality evaluation from visual diagnostics to precision measures, coverage rates and prediction performance and a description of different model- and design-based simulation designs for the evaluation. The book also features a topic-focused introduction to R and R code is provided in each chapter to explain the practical application of the described methodology. Addressed to researchers, practitioners and students who work with incomplete data, the book offers an introduction to the subject as well as a discussion of recent developments in the field. It is suitable for beginners to the topic and advanced readers alike.