S. Volgushev

发表

Structure learning for extremal tree models

Stanislav Volgushev, Sebastian Engelke, S. Volgushev, 2020, Journal of the Royal Statistical Society: Series B (Statistical Methodology).

Non‐crossing non‐parametric estimates of quantile curves

Holger Dette, Stanislav Volgushev, H. Dette, 2008 .

Multiple block sizes and overlapping blocks for multivariate time series extremes

Stanislav Volgushev, Axel Bucher, Nan Zou, 2019, The Annals of Statistics.

Group structure estimation for panel data -- a general approach

Stanislav Volgushev, Lu Yu, Jiaying Gu, 2022, 2201.01793.

On the unbiased asymptotic normality of quantile regression with fixed effects

Stanislav Volgushev, Jiaying Gu, A. Galvao, 2018, Journal of Econometrics.

Panel data quantile regression with grouped fixed effects

Stanislav Volgushev, Jiaying Gu, S. Volgushev, 2018, Journal of Econometrics.

Learning extremal graphical structures in high dimensions

Stanislav Volgushev, Sebastian Engelke, Michael Lalancette, 2021, 2111.00840.

Fighting Noise with Noise: Causal Inference with Many Candidate Instruments

Stanislav Volgushev, Linbo Wang, Dehan Kong, 2022, 2203.09330.

Distributed inference for quantile regression processes

Guang Cheng, Stanislav Volgushev, Shih-Kang Chao, 2017, The Annals of Statistics.

Model assessment for time series dynamics using copula spectral densities: A graphical tool

Tobias Kley, Stefan Birr, Stanislav Volgushev, 2018, J. Multivar. Anal..

Nonparametric comparison of quantile curves: a stochastic process approach

H. Dette, J. Wagener, S. Volgushev, 2013 .

Comparing Conditional Quantile Curves

H. Dette, J. Wagener, S. Volgushev, 2011 .

TESTING FOR HOMOGENEITY IN MIXTURE MODELS

R. Koenker, Jiaying Gu, S. Volgushev, 2013, Econometric Theory.

WHEN UNIFORM WEAK CONVERGENCE FAILS: EMPIRICAL PROCESSES FOR DEPENDENCE FUNCTIONS AND RESIDUALS VIA EPI- AND HYPOGRAPHS 1

J. Segers, Stanislav Volgushev, Axel Bücher, 2014 .

Empirical and sequential empirical copula processes under serial dependence

Stanislav Volgushev, Axel Bücher, Axel Bücher, 2011, J. Multivar. Anal..

Misspecification in copula- based regression

Ria Van Hecke, H. Dette, S. Volgushev, 2013, 1310.8037.

Some Comments on Copula-Based Regression

Holger Dette, Stanislav Volgushev, H. Dette, 2014 .

ON SELF‐NORMALIZATION FOR CENSORED DEPENDENT DATA

X. Shao, S. Volgushev, Yinxiao Huang, 2015 .

Non-parametric Quantile Regression for Censored Data

S. Volgushev, 2009 .

An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias pdf

Krishnakumar Balasubramanian, Murat A. Erdogdu, Lu Yu, 2020, NeurIPS.

Onset Dynamics of Action Potentials in Rat Neocortical Neurons and Identified Snail Neurons: Quantification of the Difference

Fred Wolf, Stanislav Volgushev, Maxim Volgushev, 2008, PloS one.

Fourier Analysis of Serial Dependence Measures

H. Dette, Stanislav Volgushev, R. Van Hecke, 2017, 1703.04320.

SFB 823 Fourier analysis of serial dependence measures

H. Dette, S. Volgushev, 2017 .

Quantile Spectral Processes: Asymptotic Analysis and Inference

Holger Dette, Tobias Kley, Stanislav Volgushev, 2014, 1401.8104.

Quantile spectral processes: asymptotic analysis and inference. 1770-1807.

H. Dette, M. Hallin, Stanislav Volgushev, 2016 .

SFB 823 Quantile spectral processes : Asymptotic analysis and inference

H. Dette, M. Hallin, Stanislav Volgushev, 2014 .

Of Copulas, Quantiles, Ranks and Spectra - An L1-Approach to Spectral Analysis

Holger Dette, Tobias Kley, Stanislav Volgushev, 2011, 1111.7205.

SMOOTHED QUANTILE REGRESSION PROCESSES FOR BINARY RESPONSE MODELS

S. Volgushev, 2013, Econometric Theory.

The integrated copula spectrum

Ria Van Hecke, H. Dette, M. Hallin, 2021, The Annals of Statistics.

The Effects of Works Councils on Overtime Hours

Kornelius Kraft, R. Gralla, S. Volgushev, 2017 .

The independence process in conditional quantile location-scale models and an application to testing for monotonicity

M. Birke, N. Neumeyer, S. Volgushev, 2016, 1609.07696.

Dose response signal detection under model uncertainty

Holger Dette, Frank Bretz, Stanislav Volgushev, 2015, Biometrics.

Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance

Murat A. Erdogdu, Nuri Mert Vural, K. Balasubramanian, 2022, COLT.

Nonparametric quantile regression for twice censored data

H. Dette, S. Volgushev, 2010, 1007.3376.

Quantile Processes for Semi and Nonparametric Regression

Guang Cheng, Stanislav Volgushev, Shih-Kang Chao, 2016, 1604.02130.

Equivalence of dose response curves

Holger Dette, Frank Bretz, Stanislav Volgushev, 2015, 1505.05266.

Energy‐efficient encoding by shifting spikes in neocortical neurons

Stanislav Volgushev, Maxim Volgushev, Tatjana Tchumatchenko, 2013, The European journal of neuroscience.

A Subsampled Double Bootstrap for Massive Data

Srijan Sengupta, Stanislav Volgushev, Xiaofeng Shao, 2015, 1508.01126.

Rank-based estimation under asymptotic dependence and independence, with applications to spatial extremes

Sebastian Engelke, Michael Lalancette, S. Volgushev, 2020, The Annals of Statistics.

Inference for change points in high-dimensional data via selfnormalization

Changbo Zhu, X. Shao, Runmin Wang, 2019, The Annals of Statistics.

Model identication for dose response signal detection

H. Dette, F. Bretz, Stanislav Volgushev, 2012 .

On Wigner-Ville Spectra and the Unicity of Time-Varying Quantile-Based Spectral Densities

H. Dette, M. Hallin, Stanislav Volgushev, 2016 .

On Wigner–Ville Spectra and the Uniqueness of Time‐Varying Copula‐Based Spectral Densities

H. Dette, M. Hallin, Tobias Kley, 2016, 1611.07253.

Quantile spectral analysis for locally stationary time series

Holger Dette, Tobias Kley, Stefan Birr, 2014, 1404.4605.

SFB 823 Quantile spectral analysis for locally stationary time series D iscussion P aper

H. Dette, M. Hallin, Tobias Kley, 2014 .

Submitted to the Annals of Statistics INFERENCE FOR CHANGE POINTS IN HIGH DIMENSIONAL DATA By

X. Shao, Runmin Wang, S. Volgushev, 2019 .

SFB 823 Significance testing in quantile regression D iscussion P aper

H. Dette, Stanislav Volgushev, Natalie Neumeyer, 2012 .

Equivalence of Regression Curves

H. Dette, F. Bretz, Stanislav Volgushev, 2018 .

Modulation of the amplitude of γ‐band activity by stimulus phase enhances signal encoding

K. Obermayer, M. Volgushev, A. Malyshev, 2011, The European journal of neuroscience.

When uniform weak convergence fails: empirical processes for dependence functions via epi- and hypographs

Johan Segers, Stanislav Volgushev, Axel Bucher, 2013, 1305.6408.

Weak convergence of the empirical copula process with respect to weighted metrics

Stanislav Volgushev, Axel Bucher, Betina Berghaus, 2014, 1411.5888.

Concentration bounds for the extremal variogram

Stanislav Volgushev, Sebastian Engelke, Michael Lalancette, 2021 .

Nonparametric analysis of covariance using quantile curves

H. Dette, J. Wagener, S. Volgushev, 2009 .

A test for Archimedeanity in bivariate copula models

Holger Dette, Stanislav Volgushev, Axel Bücher, 2011, J. Multivar. Anal..

Testing relevant hypotheses in functional time series via self‐normalization

H. Dette, Stanislav Volgushev, K. Kokot, 2018, Journal of the Royal Statistical Society: Series B (Statistical Methodology).

An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias

Murat A. Erdogdu, K. Balasubramanian, Lu Yu, 2020, NeurIPS.

SFB 823 Nonparametric analysis of covariance using quantile curves D is

H. Dette, J. Wagener, S. Volgushev, 2009 .