Moritz Jirak

Uniform change point tests in high dimension

Consider

Adaptive function estimation in nonparametric regression with one-sided errors

d

Limit theorems for aggregated linear processes

dependent change point tests, each based on a CUSUM-statistic. We provide an asymptotic theory that allows us to deal with the maximum over all test statistics as both the sample size

Simultaneous confidence bands for Yule–Walker estimators and order selection

n

Nonparametric change-point analysis of volatility

and

Volatility estimation under one-sided errors with applications to limit order books

d

Optimal eigen expansions and uniform bounds

tend to infinity. We achieve this either by a consistent bootstrap or an appropriate limit distribution. This allows for the construction of simultaneous confidence bands for dependent change point tests, and explicitly allows us to determine the location of the change both in time and coordinates in high-dimensional time series. If the underlying data has sample size greater or equal

Asymptotic behavior of weakly dependent aggregated processes

n

Rate of convergence for Hilbert space valued processes

for each test, our conditions explicitly allow for the large

Optimal Rate of Convergence for Empirical Quantiles and Distribution Functions for Time Series

d