4 papers
Central limit theorems for the outputs of fully convolutional neural networks with time series input
Annika Betken, Giorgio Micali, Johannes Schmidt-Hieber
Deep learning is widely deployed for time series learning tasks such as classification and forecasting. Despite the empirical successes, only little theory has been developed so fa…
Change-point tests for the tail parameter of Long Memory Stochastic Volatility time series
Annika Betken, Davide Giraudo, Rafał Kulik
We consider a change-point test based on the Hill estimator to test for structural changes in the tail index of Long Memory Stochastic Volatility time series. In order to determine…
Rank-based change-point analysis for long-range dependent time series
Annika Betken, Martin Wendler
We consider change-point tests based on rank statistics to test for structural changes in long-range dependent observations. Under the hypothesis of stationary time series and unde…
Testing for Change in Stochastic Volatility with Long Range Dependence
Annika Betken, Rafał Kulik
In this paper, change-point problems for long memory stochastic volatility models are considered. A general testing problem which includes various alternative hypotheses is discuss…