New Preprint: Change-Point Detection and Bootstrap for Hilbert Space Valued Random Fields

November 10th, 2015

A new preprint joint with Béatrice Bucchia about “change-point detection and bootstrap for Hilbert space valued random fields” is online at arXiv.

Abstract: The problem of testing for the presence of epidemic changes in random fields is investigated. In order to be able to deal with general changes in the marginal distribution, a Cramér-von-Mises-type test is introduced which is based on Hilbert space theory. A functional central limit theorem for ρ-mixing Hilbert space valued random fields is proven. In order to avoid the estimation of the long-run variance and obtain critical values, Shao’s dependent wild bootstrap method is adapted to this context. For this, a joint functional central limit theorem for the original and the bootstrap sample is shown. Finally, the theoretic results are supplemented by a short simulation study.

Letter to editor: Common influencing factors are no evidence of association

Oktober 25th, 2015

A comment on an article by Callander, Newman, and Holt (2015) appeared in the Archives of Sexual Behavior pointing out a statistical fallacy: A common set of influencing factors can exist for two (or more) variables, while the random variables are uncorrelated or even independent. This is possible because after controlling for explanatory variables, the remaining partial correlation might be negative.

Link to my letter to the editor.

New preprint: Subsampling for General Statistics under Long Range Dependence

September 23rd, 2015

A new preprint joint with Annika Betken about “Subsampling for General Statistics under Long Range Dependence” is online on arXiv.

Abstract: Subsampling for General Statistics under Long Range DependenceIn the statistical inference for long range dependent time series, the shape of the limit distribution typically dependents on unknown parameters. Therefore, we propose to use subsampling. We show the validity of subsampling for general statistics and long range dependent subordinated Gaussian processes, which satisfy mild regularity conditions. We apply our method to a self-normalized change-point test statistic and investigate the finite sample properties in a simulation study.

New preprint: A Robust Method for Shift Detection in Time Series

Juni 12th, 2015

A new preprint joint with Herold Dehling and Roland Fried about “A Robust Method for Shift Detection in Time Series” is online at arXiv.

Abstract: We present a robust test for change-points in time series which is based on the two-sample Hodges-Lehmann estimator. We develop new limit theory for a class of statistics based on the two-sample U-quantile processes, in the case of short range dependent observations. Using this theory we can derive the asymptotic distribution of our test statistic under the null hypothesis. We study the finite sample properties of our test via a simulation study and compare the test with the classical CUSUM test and a test based on the Wilcoxon-Mann-Whitney statistic.

New preprint: Bootstrap for U-Statistics: A new approach

Mai 28th, 2015

A new preprint joint with Olimjon Sh. Sharipov and Johannes Tewes about “Bootstrap for U-Statistics: A new approach” is online at arXiv.

Abstract: Bootstrap for nonlinear statistics like U-statistics of dependent data has been studied by several authors. This is typically done by producing a bootstrap version of the sample and plugging it into the statistic. We suggest an alternative approach of getting a bootstrap version of U-statistics, which can be described as a compromise between bootstrap and subsampling. We will show the consistency of the new method and compare its finite sample properties in a simulation study.

New preprint: Studentized sequential U-quantiles under dependence with applications to change-point analysis

März 30th, 2015

A new preprint joint with Daniel Vogel about “Studentized sequential U-quantiles under dependence with applications to change-point Analysis” is online at arXiv.

Abstract: Many popular robust estimators are U-quantiles, most notably the Hodges-Lehmann location estimator and the Q_n scale estimator. We prove a functional central limit theorem for the sequential U-quantile process without any moment assumptions and under weak short-range dependence conditions. We further devise an estimator for the long-run variance and show its consistency, from which the convergence of the studentized version of the sequential U-quantile process to a standard Brownian motion follows. This result can be used to construct CUSUM-type change-point tests based on U-quantiles, which do not rely on bootstrapping procedures. We demonstrate this approach in detail at the example of the Hodges-Lehmann estimator for robustly detecting changes in the central location. A simulation study confirms the very good robustness and efficiency properties of the test. Two real-life data sets are analyzed.

New preprint: Sequential block bootstrap in a Hilbert space with application to change point analysis

Dezember 20th, 2014

A new preprint joint with Olimjon Sh. Sharipov and Johannes Tewes about “Sequential block bootstrap in a Hilbert space with application to change point analysis” is online at arXiv.

Abstract: A new test for structural changes in functional data is investigated. It is based on Hilbert space theory and critical values are deduced from bootstrap iterations. Thus a new functional central limit theorem for the block bootstrap in a Hilbert space is required. The test can also be used to detect changes in the marginal distribution of random vectors, which is supplemented by a simulation study. Our methods are applied to hydrological data from Germany.

New preprint: Multivariate generalized linear-statistics of short range dependent data

Dezember 20th, 2014

A new preprint joint with Svenja Fischer and Roland Fried about “Multivariate generalized linear-statistics of short range dependent data” is online at arXiv.

Abstract: Generalized linear (GL-) statistics are defined as functionals of an U-quantile process and unify different classes of statistics such as U-statistics and L-statistics. We derive a central limit theorem for GL-statistics of strongly mixing sequences and arbitrary dimension of the underlying kernel. For this purpose we establish a limit theorem for U-statistics and an invariance principle for U-processes together with a convergence rate for the remaining term of the Bahadur representation. An application is given by the generalized median estimator for the tail-parameter of the Pareto distribution, which is commonly used to model exceedances of high thresholds. We use subsampling to calculate confidence intervals and investigate its behaviour under independence and strong mixing in simulations.

New preprint: The sequential empirical process of a random walk in random scenery

Oktober 12th, 2014

A new preprint about “The sequential empirical process of a random walk in random scenery” is online at arXiv.

Abstract: A random walk in random scenery $(Y_n)_{n\in\N}$ is given by $Y_n=\xi_{S_n}$ for a random walk $(S_n)_{n\in\N}$ and iid random variables $(\xi(n))_{n\in\N}$. In this paper, we will show the weak convergence of the sequential empirical process, i.e. the centered and rescaled empirical distribution function. The limit process shows a new type of behavior, combining properties of the limit in form independent case (roughness of the paths) and of the long range dependent case (self-similarity).

New preprint: Simplified simplicial depth for regression and autoregressive growth processes

Oktober 12th, 2014

A new preprint joint with Christoph P. Kustosz and  Christine H. Müller about “Simplified simplicial depth for regression and autoregressive growth processes” is online as a SFB 823 discussion paper.

Abstract: We simplify simplicial depth for regression and autoregressive growth processes in two directions. At first we show that often simplicial depth reduces to counting the subsets with alternating signs of the residuals. The second simplification is given by not regarding all subsets of residuals. By consideration of only special subsets of residuals, the asymptotic distributions of the simplified simplicial depth notions are normal distributions so that tests and confidence intervals can be derived easily. We propose two simplifications for the general case and a third simplification for the special case where two parameters are unknown. Additionally, we derive conditions for the consistency of the tests. We show that the simplified depth notions can be used for polynomial regression, for several nonlinear regression models, and for several autoregressive growth processes. We compare the efficiency and robustness of the different simplified versions by a simulation study concerning the Michaelis-Menten model and a nonlinear autoregressive process of order one.