Springer Series in Statistics Alho/Spencer: Statistical Demography and Forecasting. Keiding: Statistical Models Based on Counting Processes. Atkinson/Riani: Robust Diagnostic Regression Analysis. Bucklew: Introduction to Rare Event Simulation. Undoubtly, his stamp is on this book and we wish to take this opportunity for honoring his memory. Frédéric Ferraty Philippe Vieu Toulouse, France January, 2006. VII. Preface List of Abbreviations and Symbols List of Figures. XVII XIX. Part I Statistical Background for Nonparametric Statistics and Functional Data 1.

They are co-founders and co-organizers of the working group STAPH which acquired an international reputation for functional and operatorial statistics. They are authors of many international publications in nonparametric inference as well as functional data analysis. Their scientific works are based on extensive collaborations both with academic statisticians and with scientists from other areas. This book presents new nonparametric staustical methods for samples of functional dat. .

Nonparametric Functional Data Analysis explores nonparametric methods as that can be applied to functional data, developing new methods and providing theoretical results for the conditional and unconditional mean, median, and mode for independent and dependent functional data. Rather than set application against theory, this book is really an interface of these two features of statistics. A special effort has been made in writing this book to accommodate several levels of reading. The computational aspects are oriented toward practitioners whereas open problems emerging from this new field of statistics will attract P.

Frédéric Ferraty, Philippe Vieu. Springer Science & Business Media, 22 нояб. Modern apparatuses allow us to collect samples of functional data, mainly curves but also images. On the other hand, nonparametric statistics produces useful tools for standard data exploration.

Nonparametric Functional Data Analysis: Theory and Practice (Springer Series in Statistics). Frédéric Ferraty, Philippe Vieu. Скачать (pdf, . 5 Mb).

Author : Frédéric Ferraty,Philippe Vieu. Publisher : Springer New York. R. 0,154 on ( Rs. s may apply Shipping Charges) R. 2,359 kart. At the same time it shows how functional data can be studied through parameter-free statistical ideas, and offers an original presentation of new nonparametric statistical methods for functional data analysis. Users who liked this book, also liked.

PDF On Jan 1, 2006, Frédéric Ferraty and others published Nonparametric Functional Data Analysis: Theory and Practice. Fr´ed´eric Ferraty and Philippe Vieu. Nonparametric Functional Data. Berlin Heidelberg New York. This work is the fruit of recent advances concerning both nonparametric sta-. tistical modelling and functional variables and is based on various publica

Nonparametric Functional Data Analysis explores nonparametric methods as. they can be applied to functional data, developing new methods and provid. parametric Statistics and Functional Data, comprises four chapters and defines. nonparametric functional data analysis and the different semimetrics that the. authors consider. Tests of Noncorrelation between Multivariate Time Series Longitudinal Studies with Outcome-Dependent follow-up: Models and Bayesian Regression Bayesian Curve Classification Using Wavelets Robust Truncated Hinge Loss Support Vector Machines Functional Principal Component Regression and Functional Partial Least Squares On Directional Regression for Dimension Reduction.

Introduction to functional nonparametric statistics. Some functional datasets and associated statistical problematics. What is a well adapted space for functional data?. source: Nielsen Book Data)9780387300528. Springer series in statistics.

by Frédéric Ferraty, Philippe Vieu. Series: Springer Series in Statistics. For more help see the Common Knowledge help page. Nonparametric Functional Data Analysis: Theory and Practice.