765 research outputs found

    Bahadur Representation for U-Quantiles of Dependent Data

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    U-quantiles are applied in robust statistics, like the Hodges-Lehmann estimator of location for example. They have been analyzed in the case of independent random variables with the help of a generalized Bahadur representation. Our main aim is to extend these results to U-quantiles of strongly mixing random variables and functionals of absolutely regular sequences. We obtain the central limit theorem and the law of the iterated logarithm for U-quantiles as straightforward corollaries. Furthermore, we improve the existing result for sample quantiles of mixing data

    The Sequential Empirical Process of a Random Walk in Random Scenery

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    A random walk in random scenery (Yn)nN(Y_n)_{n\in\mathbb{N}} is given by Yn=ξSnY_n=\xi_{S_n} for a random walk (Sn)nN(S_n)_{n\in\mathbb{N}} and iid random variables (ξn)nZ(\xi_n)_{n\in\mathbb{Z}}. 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 the independent case (roughness of the paths) and in the long range dependent case (self-similarity)

    U-Processes, U-Quantile Processes and Generalized Linear Statistics of Dependent Data

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    Generalized linear statistics are an unifying class that contains U-statistics, U-quantiles, L-statistics as well as trimmed and winsorized U-statistics. For example, many commonly used estimators of scale fall into this class. GL-statistics only have been studied under independence; in this paper, we develop an asymptotic theory for GL-statistics of sequences which are strongly mixing or L^1 near epoch dependent on an absolutely regular process. For this purpose, we prove an almost sure approximation of the empirical U-process by a Gaussian process. With the help of a generalized Bahadur representation, it follows that such a strong invariance principle also holds for the empirical U-quantile process and consequently for GL-statistics. We obtain central limit theorems and laws of the iterated logarithm for U-processes, U-quantile processes and GL-statistics as straightforward corollaries.Comment: 24 page

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

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    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 Cram\'er-von Mises type test is introduced which is based on Hilbert space theory. A functional central limit theorem for ρ\rho-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

    Studentized U-quantile processes under dependence with applications to change-point analysis

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    Many popular robust estimators are UU-quantiles, most notably the Hodges-Lehmann location estimator and the QnQ_n scale estimator. We prove a functional central limit theorem for the sequential UU-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 UU-quantile process to a standard Brownian motion follows. This result can be used to construct CUSUM-type change-point tests based on UU-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

    Law of the Iterated Logarithm for U-Statistics of Weakly Dependent Observations

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    The law of the iterated logarithm for partial sums of weakly dependent processes was intensively studied by Walter Philipp in the late 1960s and 1970s. In this paper, we aim to extend these results to nondegenerate U-statistics of data that are strongly mixing or functionals of an absolutely regular process.Comment: typos corrrecte
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