79 citations · 280 across the 39 of their papers we have counts for
25 papers · 1 filter
Limiting properties of monotone rearrangements of estimators when the truth is flat
Holger Dette, Marius Kroll, Stanislav Volgushev
Monotone rearrangements provide a simple way to enforce shape constraints of an estimator, but existing distributional theory does not cover flat regions, where the target induces…
Adaptivity of the NPMLE to finitely discrete mixing distributions in Gaussian/Poisson mixtures
Yan Zhang, Stanislav Volgushev
We study the nonparametric maximum likelihood estimator (NPMLE) for Gaussian and Poisson mixture models, assuming the support of the true mixing distribution lies in a fixed bounde…
Empirical tail dependence functions in high dimensions: uniform linearizations and inference
Axel Bücher, Yeonjoon Choi, Katharina Effertz +1
The analysis of extremal dependence in high dimensions is a key challenge in modern extreme-value statistics. Existing methodology primarily focuses on modeling and estimation of e…
On a surprising behavior of the likelihood ratio test in non-parametric mixture models
Yan Zhang, Stanislav Volgushev
We study the likelihood ratio test in general mixture models where the base density is parametric, the null is a known fixed mixing distribution, and the alternative is a general m…
The integrated copula spectrum
Yuichi Goto, Tobias Kley, Ria Van Hecke +3
Frequency domain methods form a ubiquitous part of the statistical toolbox for time series analysis. In recent years, considerable interest has been given to the development of new…
Learning extremal graphical structures in high dimensions
Sebastian Engelke, Michaël Lalancette, Stanislav Volgushev
Extremal graphical models encode the conditional independence structure of multivariate extremes. Key statistics for learning extremal graphical structures are empirical extremal v…
Rank-based Estimation under Asymptotic Dependence and Independence, with Applications to Spatial Extremes
Michaël Lalancette, Sebastian Engelke, Stanislav Volgushev
Multivariate extreme value theory is concerned with modeling the joint tail behavior of several random variables. Existing work mostly focuses on asymptotic dependence, where the p…
Multiple block sizes and overlapping blocks for multivariate time series extremes
Nan Zou, Stanislav Volgushev, Axel Bücher
Block maxima methods constitute a fundamental part of the statistical toolbox in extreme value analysis. However, most of the corresponding theory is derived under the simplifying…
Inference for Change Points in High Dimensional Data via Self-Normalization
Runmin Wang, Changbo Zhu, Stanislav Volgushev +1
This article considers change point testing and estimation for a sequence of high-dimensional data. In the case of testing for a mean shift for high-dimensional independent data, w…
On Second Order Conditions in the Multivariate Block Maxima and Peak over Threshold Method
Axel Bücher, Stanislav Volgushev, Nan Zou
Second order conditions provide a natural framework for establishing asymptotic results about estimators for tail related quantities. Such conditions are typically tailored to the…
Fourier analysis of serial dependence measures
Ria van Hecke, Stanislav Volgushev, Holger Dette
Classical spectral analysis is based on the discrete Fourier transform of the auto-covariances. In this paper we investigate the asymptotic properties of new frequency domain metho…
Distributed inference for quantile regression processes
Stanislav Volgushev, Shih-Kang Chao, Guang Cheng
The increased availability of massive data sets provides a unique opportunity to discover subtle patterns in their distributions, but also imposes overwhelming computational challe…
On Wigner-Ville Spectra and the Unicity of Time-Varying Quantile-Based Spectral Densities
Stefan Birr, Holger Dette, Marc Hallin +2
The unicity of the time-varying quantile-based spectrum proposed in Birr et al. (2016) is established via an asymptotic representation result involving Wigner-Ville spectra.
The independence process in conditional quantile location-scale models and an application to testing for monotonicity
Melanie Birke, Natalie Neumeyer, Stanislav Volgushev
In this paper the nonparametric quantile regression model is considered in a location-scale context. The asymptotic properties of the empirical independence process based on covari…
Quantile Processes for Semi and Nonparametric Regression
Shih-Kang Chao, Stanislav Volgushev, Guang Cheng
A collection of quantile curves provides a complete picture of conditional distributions. Properly centered and scaled versions of estimated curves at various quantile levels give…
Weak convergence of the empirical copula process with respect to weighted metrics
Axel Bücher, Betina Berghaus, Stanislav Volgushev
The empirical copula process plays a central role in the asymptotic analysis of many statistical procedures which are based on copulas or ranks. Among other applications, results r…
Quantile Spectral Analysis for Locally Stationary Time Series
Stefan Birr, Stanislav Volgushev, Tobias Kley +2
Classical spectral methods are subject to two fundamental limitations: they only can account for covariance-related serial dependencies, and they require second-order stationarity.…
Quantile spectral processes: Asymptotic analysis and inference
Tobias Kley, Stanislav Volgushev, Holger Dette +1
Quantile- and copula-related spectral concepts recently have been considered by various authors. Those spectra, in their most general form, provide a full characterization of the c…
A general approach to the joint asymptotic analysis of statistics from sub-samples
Stanislav Volgushev, Xiaofeng Shao
In time series analysis, statistics based on collections of estimators computed from sub-samples play a crucial role in an increasing variety of important applications. Proving res…
When uniform weak convergence fails: Empirical processes for dependence functions and residuals via epi- and hypographs
Axel Bücher, Johan Segers, Stanislav Volgushev
In the past decades, weak convergence theory for stochastic processes has become a standard tool for analyzing the asymptotic properties of various statistics. Routinely, weak conv…
Censored quantile regression processes under dependence and penalization
Stanislav Volgushev, Jens Wagener, Holger Dette
We consider quantile regression processes from censored data under dependent data structures and derive a uniform Bahadur representation for those processes. We also consider cases…
Empirical and sequential empirical copula processes under serial dependence
Axel Bücher, Stanislav Volgushev
The empirical copula process plays a central role for statistical inference on copulas. Recently, Segers (2011) investigated the asymptotic behavior of this process under non-restr…
Of copulas, quantiles, ranks and spectra: An -approach to spectral analysis
Holger Dette, Marc Hallin, Tobias Kley +1
In this paper, we present an alternative method for the spectral analysis of a univariate, strictly stationary time series . We define a "new" spectrum…
A test for Archimedeanity in bivariate copula models
Axel Bücher, Holger Dette, Stanislav Volgushev
We propose a new test for the hypothesis that a bivariate copula is an Archimedean copula. The test statistic is based on a combination of two measures resulting from the character…