activity
20162025
most citedPrincipal component analysis of periodically correlated functional time series

2 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

math.ST2025

Prokhorov Metric Convergence of the Partial Sum Process for Reconstructed Functional Data

Tim Kutta, Piotr Kokoszka

Motivated by applications in functional data analysis, we study the partial sum process of sparsely observed, random functions. A key novelty of our analysis are bounds for the dis…

stat.ME2024

Detection of a structural break in intraday volatility pattern

Piotr Kokoszka, Tim Kutta, Neda Mohammadi +2

We develop theory leading to testing procedures for the presence of a change point in the intraday volatility pattern. The new theory is developed in the framework of Functional Da…

stat.ME20232 cited

Functional diffusion driven stochastic volatility model

Piotr Kokoszka, Neda Mohammadi, Haonan Wang +1

We propose a stochastic volatility model for time series of curves. It is motivated by dynamics of intraday price curves that exhibit both between days dependence and intraday pric…

math.ST2016

Change point detection in heteroscedastic time series

Tomasz Gorecki, Lajos Horvath, Piotr Kokoszka

Many time series exhibit changes both in level and in variability. Generally, it is more important to detect a change in the level, and changing or smoothly evolving variability ca…

stat.ME20162 cited

Principal component analysis of periodically correlated functional time series

Łukasz Kidziński, Piotr Kokoszka, Neda Mohammadi Jouzdani

Within the framework of functional data analysis, we develop principal component analysis for periodically correlated time series of functions. We define the components of the abov…