A new smoothing approach in FDA

Description

Functional data analysis is an ever-growing field in statistics where underlying
data structures are believed to be functional rather than discrete. Of course, in
real life all data is sampled discretely and one needs to take preprocessing steps
in order to transform them into their supposedly underlying smooth functional
form. The most commonly used methods of obtaining smooth functions employ
fitting curves using B-spline and Fourier bases with a roughness penalty. How-
ever, these methods tend to produce residuals that show unwanted correlation
structures. On the hunt for a more canonical approach, we are applying the
theory of factor models in order to obtain estimates for the underlying smooth
function of the given data. By its very nature, our approach produces uncor-
related residuals. We aim to investigate this idea further by using simulation
studies and real life data. In this talk, we wish to present our findings and fail-
ures in order to give a deeper insight into whether factor models and functional
data analysis have more common ground than commonly assumed.

Period	11 Sep 2018 - 14 Sep 2018
Held at	Statistische Woche 2018
Event type	Conference
Location	Linz, Austria
Degree of Recognition	International