{"product_id":"functional-data-analysis-with-r-9781032244716","title":"Functional Data Analysis with R","description":"\u003cp\u003eEmerging 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. \u003cb\u003eFunctional Data Analysis with R\u003c\/b\u003e 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.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eFeatures: \u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e \u003cli\u003eFunctional regression models receive a modern treatment that allows extensions to many practical scenarios and development of state-of-the-art software\u003c\/li\u003e \u003cli\u003eThe connection between functional regression, penalized smoothing, and mixed effects models is used as the cornerstone for inference\u003c\/li\u003e \u003cli\u003eMultilevel, longitudinal, and structured functional data are discussed with emphasis on emerging functional data structures\u003c\/li\u003e \u003cli\u003eMethods for clustering functional data before and after smoothing are discussed\u003c\/li\u003e \u003cli\u003eMultiple 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 study\u003c\/li\u003e \u003cli\u003eStep-by-step software implementations are included, along with a supplementary website (www.FunctionalDataAnalysis.com) featuring software, data, and tutorials\u003c\/li\u003e \u003cli\u003eMore than 100 plots for visualization of functional data are presented\u003c\/li\u003e \u003c\/ul\u003e\u003cp\u003e\u003cb\u003eFunctional Data Analysis with R\u003c\/b\u003e 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.\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Ciprian M. Crainiceanu,Jeff Goldsmith,Andrew LeRoux\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e CRC Press\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 03\/11\/2024\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 324\u003cbr\u003e\u003cb\u003eBinding Type:\u003c\/b\u003e Hardcover\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.77lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 10.00h x 7.00w x 0.81d\u003cbr\u003e\u003cb\u003eISBN:\u003c\/b\u003e 9781032244716\u003cbr\u003e\u003cp\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eCiprian M. Crainiceanu\u003c\/b\u003e 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, semiparametric regression, and functional data analysis with application to data generated by emerging technologies.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eJeff Goldsmith\u003c\/b\u003e is Associate Dean for Data Science and Associate Professor of Biostatistics at the Columbia University Mailman School of Public Health. His work in functional data analysis includes methodological and computational advances with applications in reaching kinematics, wearable devices, and neuroimaging.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eAndrew Leroux\u003c\/b\u003e is an Assistant Professor of Biostatistics and Informatics at the University of Colorado. His interests include the development of methodology in functional data analysis, particularly related to wearable technologies and intensive longitudinal data.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eErjia Cui\u003c\/b\u003e is an Assistant Professor of Biostatistics at the University of Minnesota. His research interests include developing functional data analysis methods and semiparametric regression models with reproducible software, with applications in wearable devices, mobile health, and imaging.\u003c\/p\u003e\u003cbr\u003e","brand":"CRC Press","offers":[{"title":"Hardcover","offer_id":43981463715955,"sku":"9.78103E+12","price":163.95,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0555\/9255\/0515\/files\/img_2e27961d-ee38-426f-87b9-e9b6a85b9b85.jpg?v=1760621882","url":"https:\/\/bookstorenmore.com\/en-de\/products\/functional-data-analysis-with-r-9781032244716","provider":"Bookstore N More","version":"1.0","type":"link"}