| Non-crossing non-parametric estimates of quantile curves H Dette, S Volgushev Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2008 | 185 | 2008 |
| Distributed inference for quantile regression processes S Volgushev, SK Chao, G Cheng | 164 | 2019 |
| Empirical and sequential empirical copula processes under serial dependence A Bücher, S Volgushev Journal of Multivariate Analysis 119, 61-70, 2013 | 100 | 2013 |
| Quantile spectral processes: Asymptotic analysis and inference T Kley, S Volgushev, H Dette, M Hallin | 91 | 2016 |
| Of copulas, quantiles, ranks and spectra: An -approach to spectral analysis H Dette, M Hallin, T Kley, S Volgushev | 88 | 2015 |
| New estimators of the Pickands dependence function and a test for extreme-value dependence A Bücher, H Dette, S Volgushev | 85 | 2011 |
| Inference for change points in high-dimensional data via selfnormalization R Wang, C Zhu, S Volgushev, X Shao The Annals of Statistics 50 (2), 781-806, 2022 | 69 | 2022 |
| An analysis of constant step size SGD in the non-convex regime: Asymptotic normality and bias L Yu, K Balasubramanian, S Volgushev, MA Erdogdu Advances in Neural Information Processing Systems 34, 4234-4248, 2021 | 58 | 2021 |
| Panel data quantile regression with grouped fixed effects J Gu, S Volgushev Journal of Econometrics 213 (1), 68-91, 2019 | 58 | 2019 |
| A subsampled double bootstrap for massive data S Sengupta, S Volgushev, X Shao Journal of the American Statistical Association 111 (515), 1222-1232, 2016 | 55 | 2016 |
| When uniform weak convergence fails: Empirical processes for dependence functions and residuals via epi-and hypographs A Bücher, J Segers, S Volgushev | 54 | 2014 |
| Equivalence of regression curves H Dette, K Möllenhoff, S Volgushev, F Bretz Journal of the American Statistical Association 113 (522), 711-729, 2018 | 50 | 2018 |
| Testing relevant hypotheses in functional time series via self-normalization H Dette, K Kokot, S Volgushev Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2020 | 49 | 2020 |
| Quantile spectral analysis for locally stationary time series S Birr, S Volgushev, T Kley, H Dette, M Hallin Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2017 | 49 | 2017 |
| Structure learning for extremal tree models S Engelke, S Volgushev Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2022 | 47 | 2022 |
| Weak convergence of the empirical copula process with respect to weighted metrics B Berghaus, A Bücher, S Volgushev | 45 | 2017 |
| Some comments on copula-based regression H Dette, R Van Hecke, S Volgushev Journal of the American Statistical Association 109 (507), 1319-1324, 2014 | 44 | 2014 |
| On the unbiased asymptotic normality of quantile regression with fixed effects AF Galvao, J Gu, S Volgushev Journal of Econometrics 218 (1), 178-215, 2020 | 39 | 2020 |
| A test for Archimedeanity in bivariate copula models A Bücher, H Dette, S Volgushev Journal of Multivariate Analysis 110, 121-132, 2012 | 36 | 2012 |
| Mirror descent strikes again: Optimal stochastic convex optimization under infinite noise variance NM Vural, L Yu, K Balasubramanian, S Volgushev, MA Erdogdu Conference on Learning Theory, 65-102, 2022 | 33 | 2022 |
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