The basic philosophy of Functional Data Analysis (FDA) is to think of the observed data functions as elements of a possibly infinite-dimensional function space. Most of the current research topics on FDA focus on advancing theoretical tools and extending existing multivariate techniques to accommodate the infinite-dimensional nature of data. This monograph reports contributions on both fronts, where a unifying inverse regression theory for both the multivariate setting and functional data from a Reproducing Kernel Hilbert Space (RKHS) prospective is developed. We proposed a stochastic multiple-index model, two RKHS-related inverse regression procedures, a ``slicing'' approach and a kernel approach, as well as an asymptotic theory were introduced to the statistical framework. Some general computational issues of FDA were discussed, Some general computational issues of FDA were discussed, which led to smoothed versions of the stochastic inverse regression methods.
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Haobo Ren received his B.S. and M.S. in statistics from Peking University, China, and Ph.D in statistics from Texas A&M University, USA. He was working in the Bell Labs as a member of technical staff, then moved to pharmaceutical industry as a biostatistician. He currently works in Regeneron Pharmaceuticals Incorporation, USA.
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Kartoniert / Broschiert. Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ren HaoboHaobo Ren received his B.S. and M.S. in statistics from Peking nUniversity, China, and Ph.D in statistics from Texas A&M nUniversity, USA. He was working in the Bell Labs as a member of ntechnical staff, then moved to pharma. Codice articolo 4964477
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The basic philosophy of Functional Data Analysis (FDA) is to think of the observed data functions as elements of a possibly infinite-dimensional function space. Most of the current research topics on FDA focus on advancing theoretical tools and extending existing multivariate techniques to accommodate the infinite-dimensional nature of data. This monograph reports contributions on both fronts, where a unifying inverse regression theory for both themultivariate setting and functional data from a Reproducing Kernel Hilbert Space (RKHS) prospective is developed. We proposed a stochastic multiple-index model, two RKHS-related inverse regression procedures, a ``slicing'' approach and a kernel approach, as well as an asymptotic theory were introduced to the statistical framework. Some general computational issues of FDA were discussed, Somegeneral computational issues of FDA were discussed, which led to smoothed versions of the stochastic inverse regression methods. Codice articolo 9783639177923
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