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  3. Sandsynlighedsregning og statistik

Statistical Methods for Handling Incomplete Data

Af: Jae Kwang Kim, Jun Shao Engelsk Paperback

Statistical Methods for Handling Incomplete Data

Af: Jae Kwang Kim, Jun Shao Engelsk Paperback
Tjek vores konkurrenters priser

Due to recent theoretical findings and advances in statistical computing, there has been a rapid development of techniques and applications in the area of missing data analysis. Statistical Methods for Handling Incomplete Data covers the most up-to-date statistical theories and computational methods for analyzing incomplete data.

Features

  • Uses the mean score equation as a building block for developing the theory for missing data analysis

  • Provides comprehensive coverage of computational techniques for missing data analysis

  • Presents a rigorous treatment of imputation techniques, including multiple imputation fractional imputation

  • Explores the most recent advances of the propensity score method and estimation techniques for nonignorable missing data

  • Describes a survey sampling application

  • Updated with a new chapter on Data Integration

    • Now includes a chapter on Advanced Topics, including kernel ridge regression imputation and neural network model imputation

    • The book is primarily aimed at researchers and graduate students from statistics, and could be used as a reference by applied researchers with a good quantitative background. It includes many real data examples and simulated examples to help readers understand the methodologies.

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    Tjek vores konkurrenters priser
    Normalpris
    kr 488
    Fragt: 39 kr
    6 - 8 hverdage
    20 kr
    Pakkegebyr
    God 4 anmeldelser på
    Tjek vores konkurrenters priser

    Due to recent theoretical findings and advances in statistical computing, there has been a rapid development of techniques and applications in the area of missing data analysis. Statistical Methods for Handling Incomplete Data covers the most up-to-date statistical theories and computational methods for analyzing incomplete data.

    Features

    • Uses the mean score equation as a building block for developing the theory for missing data analysis

    • Provides comprehensive coverage of computational techniques for missing data analysis

    • Presents a rigorous treatment of imputation techniques, including multiple imputation fractional imputation

    • Explores the most recent advances of the propensity score method and estimation techniques for nonignorable missing data

    • Describes a survey sampling application

    • Updated with a new chapter on Data Integration

      • Now includes a chapter on Advanced Topics, including kernel ridge regression imputation and neural network model imputation

      • The book is primarily aimed at researchers and graduate students from statistics, and could be used as a reference by applied researchers with a good quantitative background. It includes many real data examples and simulated examples to help readers understand the methodologies.

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      Produktdetaljer
      Sprog: Engelsk
      Sider: 380
      ISBN-13: 9781032118130
      Indbinding: Paperback
      Udgave: 2
      ISBN-10: 103211813X
      Kategori: Sandsynlighedsregning og statistik
      Udg. Dato: 29 jan 2024
      Længde: 29mm
      Bredde: 234mm
      Højde: 156mm
      Forlag: Taylor & Francis Ltd
      Oplagsdato: 29 jan 2024
      Forfatter(e): Jae Kwang Kim, Jun Shao
      Forfatter(e) Jae Kwang Kim, Jun Shao

      Kategori Sandsynlighedsregning og statistik

      ISBN-13 9781032118130

      Sprog Engelsk

      Indbinding Paperback

      Sider 380

      Udgave 2

      Længde 29mm

      Bredde 234mm

      Højde 156mm

      Udg. Dato 29 jan 2024

      Oplagsdato 29 jan 2024

      Forlag Taylor & Francis Ltd