activity
20052026
most citedOn the range of validity of the autoregressive sieve bootstrap

112 citations · 311 across the 23 of their papers we have counts for

collaborators

27 papers

math.ST2026

Frequency Domain Bootstrap for Functional Time Series

Daniel Rademacher, Jens-Peter Kreiss, Efstathios Paparoditis

A frequency domain bootstrap procedure for functional time series is proposed and applied to the class of spectral mean operators. The procedure works by first generating independe…

math.ST2025

Detecting Periodicity of a General Stationary Time Series via AR(2)-Model Fitting

Jens-Peter Kreiss, Panagiotis Maouris, Efstathios Paparoditis

Estimating the periodicity of a stationary time series via fitting a second order stationary autoregressive (AR(2)) model has been initiated by the seminal paper of Yule(1927).. We…

stat.ME2025

Predictive inference for discrete-valued time series

Maxime Faymonville, Carsten Jentsch, Efstathios Paparoditis

For discrete-valued time series, predictive inference cannot be implemented through the construction of prediction intervals to some predetermined coverage level, as this is the ca…

math.ST2024

Functional Sieve Bootstrap for the Partial Sum Process with Application to Change-Point Detection

Efstathios Paparoditis, Lea Wegner, Martin Wendler

This paper applies the functional sieve bootstrap (FSB) to estimate the distribution of the partial sum process for time series stemming from a weakly stationary functional process…

math.ST2024

Gaussian Approximation for Lag-Window Estimators and the Construction of Confidence bands for the Spectral Density

Jens-Peter Kreiss, Anne Leucht, Efstathios Paparoditis

In this paper we consider the construction of simultaneous confidence bands for the spectral density of a stationary time series using a Gaussian approximation for classical lag-wi…

stat.ME2022★ 4 cited

Frequency Domain Statistical Inference for High-Dimensional Time Series

Jonas Krampe, Efstathios Paparoditis

Analyzing time series in the frequency domain enables the development of powerful tools for investigating the second-order characteristics of multivariate processes. Parameters lik…