Stochastic Inverse Regression and Reproducing Kernel Hilbert Space: with Applications in Functional Data Analysis - Softcover

Ren, Haobo

Softcover

ISBN 10: 3639177924 ISBN 13: 9783639177923

Publisher: VDM Verlag, 2009

Synopsis

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.

About this title

About the Author

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.

Edition details

• Publisher: VDM Verlag
• Publication date: 2009
• Language: English
• ISBN 10: 3639177924
• ISBN 13: 9783639177923
• Binding: Paperback
• Number of pages: 112