Erjia (15 Ergebnisse)

- Hardcover
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- Hardcover
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Functional Data Analysis with R (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)
Crainiceanu, Ciprian M.; Goldsmith, Jeff; Leroux, Andrew; Cui, Erjia
- Hardcover
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Functional Data Analysis with R (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)
Ciprian M. Crainiceanu, Jeff Goldsmith, Andrew Leroux, Erjia Cui
- Hardcover
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Hardcover. Zustand: New.

Functional Data Analysis with R (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)
Crainiceanu, Ciprian M.; Goldsmith, Jeff; Leroux, Andrew; Cui, Erjia
- Hardcover
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Zustand: New. In.

Functional Data Analysis with R (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)
Crainiceanu, Ciprian M.; Goldsmith, Jeff; Leroux, Andrew; Cui, Erjia
- Hardcover
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Zustand: New. 1st edition NO-PA16APR2015-KAP.

- Hardcover
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- Hardcover
- Erstausgabe
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Hardback. Zustand: New. 1st. Emerging technologies generate data sets of increased size and complexity that require new or updated statistical inferential methods and scalable, reproducible software. These data sets often involve measurements of a continuous underlying process, and benefit from a functional data perspective. Fun…ctional Data Analysis with R presents many ideas for handling functional data including dimension reduction techniques, smoothing, functional regression, structured decompositions of curves, and clustering. The idea is for the reader to be able to immediately reproduce the results in the book, implement these methods, and potentially design new methods and software that may be inspired by these approaches.Features:Functional regression models receive a modern treatment that allows extensions to many practical scenarios and development of state-of-the-art softwareThe connection between functional regression, penalized smoothing, and mixed effects models is used as the cornerstone for inferenceMultilevel, longitudinal, and structured functional data are discussed with emphasis on emerging functional data structuresMethods for clustering functional data before and after smoothing are discussedMultiple new functional data sets with dense and sparse sampling designs from various application areas are presented, including the NHANES linked accelerometry and mortality data, COVID-19 mortality data, CD4 counts data and the CONTENT child growth studyStep-by-step software implementations are included, along with a supplementary website featuring software, data, and tutorialsMore than 100 plots for visualization of functional data are presentedFunctional Data Analysis with R is primarily aimed at undergraduate, master's and PhD students, as well as data scientists and researchers working on functional data analysis. The book can be read at different levels and combines state-of-the-art software, methods, and inference. It can be used for self-learning, teaching, and research, and will particularly appeal to anyone who is interested in practical methods for hands-on, problem-forward functional data analysis. The reader should have some basic coding experience, but expertise in R is not required.

- Hardcover
Anbieter: moluna, Greven, , Deutschlandmoluna
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EUR 116,65
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Zustand: New. Ciprian M. Crainiceanu is Professor of Biostatistics at Johns Hopkins University working on wearable and implantable technology (WIT), signal processing, and clinical neuroimaging. He has extensive experience in mixed effects modeling, semiparamet.

- Hardcover
Anbieter: Revaluation Books, Exeter, , Vereinigtes KönigreichRevaluation Books
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Hardcover. Zustand: Brand New. 336 pages. 9.19x6.13x1.02 inches. In Stock.

- Softcover
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Soft cover. Zustand: New. Language:Chinese.Author:Fang Erjia.Binding:Soft Cover.Publisher:China Renmin University Press.

- Hardcover
- Erstausgabe
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Hardback. Zustand: New. 1st. Emerging technologies generate data sets of increased size and complexity that require new or updated statistical inferential methods and scalable, reproducible software. These data sets often involve measurements of a continuous underlying process, and benefit from a functional data perspective. Fun…ctional Data Analysis with R presents many ideas for handling functional data including dimension reduction techniques, smoothing, functional regression, structured decompositions of curves, and clustering. The idea is for the reader to be able to immediately reproduce the results in the book, implement these methods, and potentially design new methods and software that may be inspired by these approaches.Features:Functional regression models receive a modern treatment that allows extensions to many practical scenarios and development of state-of-the-art softwareThe connection between functional regression, penalized smoothing, and mixed effects models is used as the cornerstone for inferenceMultilevel, longitudinal, and structured functional data are discussed with emphasis on emerging functional data structuresMethods for clustering functional data before and after smoothing are discussedMultiple new functional data sets with dense and sparse sampling designs from various application areas are presented, including the NHANES linked accelerometry and mortality data, COVID-19 mortality data, CD4 counts data and the CONTENT child growth studyStep-by-step software implementations are included, along with a supplementary website featuring software, data, and tutorialsMore than 100 plots for visualization of functional data are presentedFunctional Data Analysis with R is primarily aimed at undergraduate, master's and PhD students, as well as data scientists and researchers working on functional data analysis. The book can be read at different levels and combines state-of-the-art software, methods, and inference. It can be used for self-learning, teaching, and research, and will particularly appeal to anyone who is interested in practical methods for hands-on, problem-forward functional data analysis. The reader should have some basic coding experience, but expertise in R is not required.

Functional Data Analysis with R (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)
Crainiceanu, Ciprian M.; Goldsmith, Jeff; Leroux, Andrew; Cui, Erjia
- Hardcover
- Print-on-Demand
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Zustand: New. PRINT ON DEMAND.

- Hardcover
- Print-on-Demand
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Hardcover. Zustand: Brand New. 336 pages. 9.19x6.13x1.02 inches. In Stock. This item is printed on demand.