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 …

Introduction to Functional Data Analysis provides a concise textbook introduction to the field.
It explains how to analyze functional data, both at exploratory and inferential levels. It also …

As high-dimensional and high-frequency data are being collected on a large scale, the
development of new statistical models is being pushed forward. Functional data analysis …

A Universal Kriging predictor for spatially dependent functional data of a Hilbert Space Page 1
Electronic Journal of Statistics Vol. 7 (2013) 2209–2240 ISSN: 1935-7524 DOI: 10.1214/13-EJS843 …

Dynamic systems modeled by computationally intensive numerical models with time-
dependent output are common in engineering. Efficient uncertainty propagation of such …

Functional data featured by a spatial dependence structure occur in many environmental
sciences when curves are observed, for example, along time or along depth. Recently, some …

We consider the problem of predicting the spatial field of particle-size curves (PSCs) from a
sample observed at a finite set of locations within an alluvial aquifer near the city of …

Cokriging is a geostatistical technique that is used for spatial prediction when realizations of
a random field are available. If a secondary variable is cross-correlated with the primary …

A variable annuity (VA) is equity-linked annuity product that has rapidly grown in popularity
around the world in recent years. Research up to date on VA largely focuses on the …

We review recent advances in Object Oriented Spatial Statistics, a system of ideas,
algorithms and methods that allows the analysis of high dimensional and complex data …