Modern studies from a variety of fields record multiple functional observations according to
either multivariate, longitudinal, spatial, or time series designs. We refer to such data as …

This article reviews some recent developments on the inference of time series data using the
self-normalized approach. We aim to provide a detailed discussion about the use of self …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

Economic and financial data often take the form of a collection of curves observed
consecutively over time. Examples include, intraday price curves, yield and term structure …

Methodology is proposed to uncover structural breaks in functional data that is 'fully
functional'in the sense that it does not rely on dimension reduction techniques. A thorough …

In several arenas of application, it is becoming increasingly common to consider time series
of curves or functions. Many inferential procedures employed in the analysis of such data …

We develop methodology for testing relevant hypotheses about functional time series in a
tuning-free way. Instead of testing for exact equality, eg for the equality of two mean …

The bootstrap is a popular and powerful method for assessing precision of estimators and
inferential methods. However, for massive datasets that are increasingly prevalent, the …

We develop an expansion, similar in some respects to the Karhunen–Loève expansion, but
which is more suitable for functional data indexed by spatial locations on a grid. Unlike the …

A popular self-normalization (SN) approach in time series analysis uses the variance of a
partial sum as a self-normalizer. This is known to be sensitive to irregularities such as …