79 citations · 280 across the 39 of their papers we have counts for
45 papers
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…
Graph structure learning for stable processes
Florian Brück, Sebastian Engelke, Stanislav Volgushev
We introduce Ising-Hüsler-Reiss processes, a new class of multivariate Lévy processes that allows for sparse modeling of the path-wise conditional independence structure between ma…
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…
Simultaneous hypothesis testing for comparing many functional means
Colin Decker, Dehan Kong, Stanislav Volgushev
Data with multiple functional recordings at each observational unit are increasingly common in various fields including medical imaging and environmental sciences. To conduct infer…
Universality of Benign Overfitting in Binary Linear Classification
Ichiro Hashimoto, Stanislav Volgushev, Piotr Zwiernik
The practical success of deep learning has led to the discovery of several surprising phenomena. One of these phenomena, that has spurred intense theoretical research, is ``benign…
Change-point Inference for High-dimensional Heteroscedastic Data
Teng Wu, Stanislav Volgushev, Xiaofeng Shao
We propose a bootstrap-based test to detect a mean shift in a sequence of high-dimensional observations with unknown time-varying heteroscedasticity. The proposed test builds on th…
Fighting Noise with Noise: Causal Inference with Many Candidate Instruments
Xinyi Zhang, Linbo Wang, Stanislav Volgushev +1
Instrumental variable methods provide useful tools for inferring causal effects in the presence of unmeasured confounding. To apply these methods with large-scale data sets, a majo…
Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance
Nuri Mert Vural, Lu Yu, Krishnakumar Balasubramanian +2
We study stochastic convex optimization under infinite noise variance. Specifically, when the stochastic gradient is unbiased and has uniformly bounded -th moment, for some…
Spectral Clustering with Variance Information for Group Structure Estimation in Panel Data
Lu Yu, Jiaying Gu, Stanislav Volgushev
Consider a panel data setting where repeated observations on individuals are available. Often it is reasonable to assume that there exist groups of individuals that share similar e…
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…
Structure learning for extremal tree models
Sebastian Engelke, Stanislav Volgushev
Extremal graphical models are sparse statistical models for multivariate extreme events. The underlying graph encodes conditional independencies and enables a visual interpretation…
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…
An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias
Lu Yu, Krishnakumar Balasubramanian, Stanislav Volgushev +1
Structured non-convex learning problems, for which critical points have favorable statistical properties, arise frequently in statistical machine learning. Algorithmic convergence…
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…
Testing relevant hypotheses in functional time series via self-normalization
Holger Dette, Kevin Kokot, Stanislav Volgushev
In this paper we develop methodology for testing relevant hypotheses about functional time series in a tuning-free way. Instead of testing for exact equality, for example for the e…
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…
On the Unbiased Asymptotic Normality of Quantile Regression with Fixed Effects
Antonio F. Galvao, Jiaying Gu, Stanislav Volgushev
Nonlinear panel data models with fixed individual effects provide an important set of tools for describing microeconometric data. In a large class of such models (including probit,…
Model assessment for time series dynamics using copula spectral densities: a graphical tool
Stefan Birr, Tobias Kley, Stanislav Volgushev
Finding parametric models that accurately describe the dependence structure of observed data is a central task in the analysis of time series. Classical frequency domain methods pr…
Panel Data Quantile Regression with Grouped Fixed Effects
Jiaying Gu, Stanislav Volgushev
This paper introduces estimation methods for grouped latent heterogeneity in panel data quantile regression. We assume that the observed individuals come from a heterogeneous popul…
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…