| Non‐crossing non‐parametric estimates of quantile curves H Dette, S Volgushev Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2008 | 143 | 2008 |
| Empirical and sequential empirical copula processes under serial dependence A Bücher, S Volgushev Journal of Multivariate Analysis 119, 61-70, 2013 | 78 | 2013 |
| New estimators of the Pickands dependence function and a test for extreme-value dependence A Bücher, H Dette, S Volgushev Annals of statistics 39 (4), 1963-2006, 2011 | 72 | 2011 |
| Of copulas, quantiles, ranks and spectra: An -approach to spectral analysis H Dette, M Hallin, T Kley, S Volgushev Bernoulli 21 (2), 781-831, 2015 | 58 | 2015 |
| Distributed inference for quantile regression processes S Volgushev, SK Chao, G Cheng The Annals of Statistics 47 (3), 1634-1662, 2019 | 46 | 2019 |
| Quantile spectral processes: Asymptotic analysis and inference T Kley, S Volgushev, H Dette, M Hallin Bernoulli 22 (3), 1770-1807, 2016 | 44 | 2016 |
| When uniform weak convergence fails: Empirical processes for dependence functions and residuals via epi-and hypographs A Bücher, J Segers, S Volgushev Annals of Statistics 42 (4), 1598-1634, 2014 | 35 | 2014 |
| 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 | 30 | 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 | 27 | 2014 |
| A test for Archimedeanity in bivariate copula models A Bücher, H Dette, S Volgushev Journal of Multivariate Analysis 110, 121-132, 2012 | 27 | 2012 |
| Equivalence of regression curves H Dette, K Möllenhoff, S Volgushev, F Bretz Journal of the American Statistical Association 113 (522), 711-729, 2018 | 25 | 2018 |
| Weak convergence of the empirical copula process with respect to weighted metrics B Berghaus, A Bücher, S Volgushev Bernoulli 23 (1), 743-772, 2017 | 23* | 2017 |
| A subsampled double bootstrap for massive data S Sengupta, S Volgushev, X Shao Journal of the American Statistical Association 111 (515), 1222-1232, 2016 | 23 | 2016 |
| Onset dynamics of action potentials in rat neocortical neurons and identified snail neurons: quantification of the difference M Volgushev, A Malyshev, P Balaban, M Chistiakova, S Volgushev, ... PLoS One 3 (4), e1962, 2008 | 21 | 2008 |
| A general approach to the joint asymptotic analysis of statistics from sub-samples S Volgushev, X Shao Electronic Journal of Statistics 8 (1), 390-431, 2014 | 17 | 2014 |
| Significance testing in quantile regression S Volgushev, M Birke, H Dette, N Neumeyer Electronic Journal of Statistics 7, 105-145, 2013 | 16 | 2013 |
| Regulatory assessment of drug dissolution profiles comparability via maximum deviation K Moellenhoff, H Dette, E Kotzagiorgis, S Volgushev, O Collignon Statistics in medicine 37 (20), 2968-2981, 2018 | 15 | 2018 |
| Panel data quantile regression with grouped fixed effects J Gu, S Volgushev Journal of Econometrics 213 (1), 68-91, 2019 | 14 | 2019 |
| Testing for homogeneity in mixture models J Gu, R Koenker, S Volgushev arXiv preprint arXiv:1302.1805, 2013 | 14 | 2013 |
| Quantile processes for semi and nonparametric regression SK Chao, S Volgushev, G Cheng Electronic Journal of Statistics 11 (2), 3272-3331, 2017 | 13 | 2017 |
DetteProfessor für MathematikVerified email at rub.de
