Colin O. Wu, Xin Tian

Nonparametric Models for Longitudinal Data (eBook, ePUB)

With Implementation in R

• Format: ePub

Produktbeschreibung

Nonparametric Models for Longitudinal Data with Implementations in R presents a comprehensive summary of major advances in nonparametric models and smoothing methods with longitudinal data. It covers methods, theories, and applications that are particularly useful for biomedical studies in the era of big data and precision medicine.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 582
• Erscheinungstermin: 23. Mai 2018
• Englisch
• ISBN-13: 9780429939075
• Artikelnr.: 56831430

Autorenporträt

Both authors are mathematical statisticians at the National Institutes of Health (NIH) and have published extensively in statistical and biomedical journals.

Colin O. Wu earned his Ph.D. in statistics from the University of California, Berkeley (1990), and is also Adjunct Professor at the Georgetown University School of Medicine. He served as Associate Editor for Biometrics and Statistics in Medicine, and reviewer for National Science Foundation, NIH, and the U.S. Department of Veterans Affairs.

Xin Tian earned her Ph.D. in statistics from Rutgers, the State University of New Jersey (2003). She has served on various NIH committees and collaborated extensively with clinical researchers.

Inhaltsangabe

Overview of Longitudinal Analysis. Parametric and Semiparametric Methods.
Nonparametric Models for Conditional-Means. Nonparametric Models with
Missing. Functional Linear Models. Nonparametric Joint-Models with Survival
Data. Methods for Time-Dependent and Outcome-Adaptive Covariate.
Nonparametric Models for Distribution Functions. Structural Nonparametric
Methods for Quantile Regression. Appendices.