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Hierarchical Quantile Modeling : Theory, Methodology and Applications

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Title: Hierarchical Quantile Modeling : Theory, Methodology and Applications
Description: This book offers a concise and comprehensive introduction to Hierarchical Quantile Modeling, a modern statistical methodology that extends traditional hierarchical models and quantile regression techniques to analyze complex data structures often found in fields like biology, economics, and education. Unlike classic models, Hierarchical Quantile Modeling accommodates heteroscedasticity and nonparametric relationships, allowing for a detailed study of the entire conditional distribution of a response variable.The book is structured in four parts: an introduction to hierarchical modeling, a detailed look at quantile regression, an in-depth exploration of Hierarchical Quantile Modeling, and practical applications using real-world hierarchical, repeated, and clustered data. Drawing on the author's decade-long experience in research and teaching, this guide is ideal for graduate students, researchers, and practitioners. It includes examples and software guidance using R, S-plus, SAS, and SPSS, making it a valuable resource for anyone interested in advanced statistical analysis.
Authors: Maozai TIAN
Resource Type: eBook.
Subjects: Multilevel models (Statistics)
Categories: MATHEMATICS / Complex Analysis
Database: eBook Collection (EBSCOhost)
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  Data: This book offers a concise and comprehensive introduction to Hierarchical Quantile Modeling, a modern statistical methodology that extends traditional hierarchical models and quantile regression techniques to analyze complex data structures often found in fields like biology, economics, and education. Unlike classic models, Hierarchical Quantile Modeling accommodates heteroscedasticity and nonparametric relationships, allowing for a detailed study of the entire conditional distribution of a response variable.The book is structured in four parts: an introduction to hierarchical modeling, a detailed look at quantile regression, an in-depth exploration of Hierarchical Quantile Modeling, and practical applications using real-world hierarchical, repeated, and clustered data. Drawing on the author's decade-long experience in research and teaching, this guide is ideal for graduate students, researchers, and practitioners. It includes examples and software guidance using R, S-plus, SAS, and SPSS, making it a valuable resource for anyone interested in advanced statistical analysis.
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      – Code: 519.536
        Scheme: ddc
        Type: prePub
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Multilevel models (Statistics)
        Type: general
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      – TitleFull: Hierarchical Quantile Modeling : Theory, Methodology and Applications
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          Name:
            NameFull: Maozai TIAN
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          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2024
            – D: 05
              M: 03
              Type: profile
              Y: 2026
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              Value: 9782759837199
            – Type: isbn-electronic
              Value: 9782759837205
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