Inference for Functional Data with Applications

Lajos Horvath is Professor of Mathematics at the University of Utah. He has served on the editorial boards of Statistics & Probability Letters, Journal of Statistical Planning and Inference and Journal of Time Series Econometrics. He has coauthored more than 250 research papers and 3 books, including Weighted Approximations in Probability and Statistics and Limit Theorems in Change-Point Analysis (both with Miklos Csorgo). Piotr Kokoszka is Professor of Statistics at Colorado State University. He has served on the editorial boards of the journals Statistical Modelling and Computational Statistics. He has coauthored over 100 papers in areas of statistics and its applications focusing on dependent data.

From the reviews: "This is an attractive, impressive and useful book, which gives an effective account of statistical methods and theory used in functional data analysis, as applied to problems arising in many fields, including finance, biological sciences, physics, the geosciences and economics. It serves as an excellent, contemporary reference text. Functional data analysis concerns statistical inference when individual observations take the form of functions defined over some set, perhaps representing time or spatial location...Functional data analysis is a very broad, active research area. I believe that the authors succeed in their basic aim of speaking both to readers interested in methodology, who will find detailed descriptions of inferential procedures (based around the use of R) and evidence of their usefulness, and also to researchers interested in the underlying mathematics, which is presented cleanly, without too much technical fuss. It is very readable, and would provide an excellent basis for advanced study, at the graduate level, of this important and active area of statistics." (G. Alastair Young, International Statistical Review, 82, 1, 2014) "This book offers an up-to-date perspective on the booming field of statistics with functional data [often called Functional Data Analysis (FDA)]. ... this book is a timely, valuable addition to the current textbooks on FDA. It will surely find its place among the well-known references ... ." (Antonio Cuevas, Mathematical Reviews, January, 2013)

• Independent functional observations.- The functional linear model.- Dependent functional data.- References.- Index.