Stanislav Volgushev
Associate Professor
Department of Statisical Sciences
University of Toronto
Mathematical & Computational Sciences, UTM

Selected preprints

  1. Zhang, X., Wang, L., and Volgushev, S. and Kong, D.
    Fighting Noise with Noise: Causal Inference with Many Candidate Instruments
    arxiv
  2. Engelke, S., Lalancette, M. and Volgushev, S.
    Concentration bounds for the extremal variogram
    arxiv
  3. Yu, L., Gu, J. and Volgushev, S.
    Group structure estimation for non-linear panel data - a general approach
    arxiv

Methodology and theory publications

  1. Goto, Y., Kley, T., Van Hecke, R., Volgushev, S., Dette, H. and Hallin, M. (2022+)
    The integrated copula spectrum
    Annals of Statistics accepted arxiv preprint
  2. Engelke, S. and Volgushev, S. (2022)
    Structure learning for extremal tree models
    Journal of the Royal Statistical Society: Series B 84(4) 2055–2087 arxiv preprint
  3. Vural, N.M., Yu, L., Balasubramanian, K., Volgushev, S. and Erdogdu, M. (2022)
    Mirror Descent Strikes Again: Optimal Stochastic Convex Optimization under Infinite Noise Variance
    COLT 2022 (pp. 65-102). PMLR arxiv preprint
  4. Wang, R., Zhu, C., Volgushev, S. and Shao, X (2022)
    Inference for Change Points in High Dimensional Data
    Annals of Statistics 50 (2), pp 781-806 arxiv preprint
  5. Yu, L., Balasubramanian, K., Volgushev, S. and Erdogdu, M. (2021)
    An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias
    Advances in Neural Information Processing Systems 35 (NeurIPS), 2021 arxiv preprint
  6. Lalancette, M., Engelke, S. and Volgushev, S. (2021)
    Rank-based Estimation under Asymptotic Dependence and Independence, with Applications to Spatial Extremes
    Annals of Statistics 49(5): pp 2552-2576 arxiv preprint
  7. Zou, N., Volgushev, S. and Bücher, A. (2021).
    Multiple block sizes and overlapping blocks for multivariate time series extremes
    Annals of Statistics 49(1): pp 295-320 arxiv preprint
  8. Galvao, A., Gu, J. and Volgushev, S. (2020).
    On the Unbiased Asymptotic Normality of Quantile Regression with Fixed Effects
    Journal of Econometrics 218(1), pp 178-215 arxiv preprint
  9. Dette, H., Kokot, K., and Volgushev, S. (2020).
    Testing relevant hypotheses in functional time series via self-normalization
    Journal of the Royal Statistical Society: Series B 82 (3) 629-660 arxiv preprint
  10. Volgushev, S. (2020).
    Smoothed quantile regression processes for binary response models.
    Econometric Theory 36 (2), pp 292-330 arxiv preprint
  11. Gu, J. and Volgushev, S. (2019).
    Panel Data Quantile Regression with Grouped Fixed Effects
    Journal of Econometrics 213(1), pp 68-91 arxiv preprint
  12. Volgushev, S., Chao, S.-K. and Cheng, G. (2019)
    Distributed inference for quantile regression processes
    Annals of Statistics 47(3), pp 1634-1662. arxiv preprint
  13. Bücher, A., Volgushev, S. and Zou, N. (2019).
    On Second Order Conditions in the Multivariate Block Maxima and Peak over Threshold Method
    Journal of Multivariate Analysis 173, pp 604-619 arxiv preprint
  14. Birr, S., Kley, T. and Volgushev, S. (2019).
    Model assessment for time series dynamics using copula spectral densities: a graphical tool
    Journal of Multivariate Analysis 172, pp 122-146 arxiv preprint
  15. Möllenhoff, K., Dette, H., Kotzagiorgis, E. Volgushev, S. and Collignon, O. (2018).
    Regulatory assessment of drug dissolution profiles comparability via maximum deviation.
    Statistics in Medicine ; 37 (20), pp 2968–2981
  16. Dette, H., Möllenhoff, K., Volgushev, S. and Bretz , F. (2018).
    Equivalence of dose response curves.
    Journal of the American Statistical Association 113 (522), pp 711-729 arxiv preprint
  17. Gu, J., Koenker, R. and Volgushev, S. (2018).
    Testing for homogeneity in mixture models.
    Econometric Theory 34 (4), pp 850-895 arxiv preprint
  18. van Hecke, R., Volgushev, S. and Dette, H. (2018).
    Fourier analysis of serial dependence measures
    Journal of Time Series Analysis 39, pp 75-89 arxiv preprint
  19. Birr, S., Dette, H., Hallin, M., Kley, T. and Volgushev, S. (2018).
    On Wigner-Ville Spectra and the Unicity of Time-Varying Quantile-Based Spectral Densities
    Journal of Time Series Analysis 39, pp 242-250 arxiv preprint
  20. Birr, S,. Volgushev, S., Kley, T., Dette, H. and Hallin, M. (2017).
    Quantile spectral analysis for locally stationary time series.
    Journal of the Royal Statistical Society: Series B 79 (5), pp 1619–1643 arxiv preprint
  21. Chao, S.-K., Volgushev, S. and Cheng, G. (2017).
    Quantile processes for semi and nonparametric regression
    Electronic Journal of Statistics 11, pp 3272-3331 arxiv preprint
  22. Birke, M., Neumeyer, N. and Volgushev, S. (2017).
    The independence process in conditional quantile location-scale models and an application to testing for monotonicity.
    Statistica Sinica 27, pp 1815-1839 arxiv preprint
  23. Berghaus, B., Bücher, A. and Volgushev, S (2017).
    Weak convergence of the empirical copula process with respect to weighted metrics.
    Bernoulli 23 (1), 743-772
  24. Sengupta, S., Volgushev, S. and Shao, X. (2016).
    A subsampled double bootstrap for massive data.
    Journal of the American Statistical Association 111 (515), 1222-1232
  25. Kley, T., Volgushev, S., Dette, H. and Hallin, M. (2016).
    Quantile spectral processes: Asymptotic analysis and inference.
    Bernoulli 22 (3), 1770-1807
  26. Dette, H., Titoff, S., Volgushev, S. and Bretz, F. (2015).
    Model identification for dose response signal detection.
    Biometrics 71 (4), 996-1008
  27. Dette, H., Hallin, M., Kley, T. and Volgushev, S. (2015).
    Of copulas, quantiles, ranks and spectra: An $L_1$-approach to spectral analysis.
    Bernoulli Vol. 21, No. 2, 781-831
  28. Huang, Y., Volgushev, S. and Shao, X. (2015).
    On self-normalization for censored dependent data.
    Journal of Time Series Analysis Vol. 36(1), 109-124.
  29. Volgushev, S., Wagener, J. and Dette, H. (2014).
    Censored quantile regression processes under dependence and penalization.
    Electronic Journal of Statistics, Vol. 8(2), 2405-2447.
  30. Dette, H., Van Hecke, R. and Volgushev, S. (2014).
    Some comments on copula-based regression.
    Journal of the American Statistical Association , Vol. 109(507), 1319-1324.
  31. Volgushev, S. and Shao, X. (2014).
    A general approach to the joint asymptotic analysis of statistics from sub-samples.
    Electronic Journal of Statistics , Vol. 8, 390-431.
  32. Bücher, A., Segers, J. and Volgushev, S. (2014).
    When uniform weak convergence fails: Empirical processes for dependence functions and residuals via epi- and hypographs.
    Annals of Statistics , Vol. 42(4), 1598-1634
  33. Volgushev, S. and Dette, H. (2013).
    Nonparametric quantile regression for twice censored data.
    Bernoulli Vol. 19(3), 748-779.
  34. Bücher, A. and Volgushev, S. (2013).
    Empirical and sequential empirical copula processes under serial dependence.
    Journal of Multivariate Analysis , Vol. 119, 61-70.
  35. Dette, H., Wagener, J. and Volgushev, S. (2013).
    Nonparametric comparison of quantile curves: A stochastic process approach.
    Journal of Nonparametric Statistics , Vol. 25(1), 243-260.
  36. Volgushev, S., Birke, M., Dette, H and Neumeyer, N. (2013).
    Significance testing in quantile regression.
    Electronic Journal of Statistics , Vol. 7, 105-145
  37. Bücher, A., Dette, H. and Volgushev, S. (2012).
    A test for Archimedeanity in bivariate copula models.
    Journal of Multivariate Analysis , Vol. 110, 121-132.
  38. Wagener, J., Volgushev, S. and Dette, H. (2012).
    The quantile process under random censoring
    Mathematical Methods of Statistics , Vol. 21(2), 127-141.
  39. Bücher, A., Dette, H. and Volgushev, S. (2011).
    New estimators of the Pickands dependence function and a test for extreme-value dependence.
    Annals of Statistics , Vol. 39, No. 4, 1963-2006.
  40. Dette, H., Wagener, J. and Volgushev, S. (2011).
    Comparing conditional quantile curves.
    Scandinavian Journal of Statistics , Vol. 38, 63-88.
  41. Dette, H. and Volgushev, S. (2008).
    Non-crossing nonparametric estimates of quantile curves
    Journal of the Royal Statistical Society: Series B , Vol. 70(3), 609-627.

Collaborative Publications

  1. Gralla, R., Kraft, K. and Volgushev, S. (2017).
    The effects of works councils on overtime hours - a censored quantile regression approach.
    Scottish Journal of Political Economy, Vol 64, pp 143-168
  2. Malyshev, A., Tchumachenko, T., Volgushev, S. and Volgushev, M. (2013).
    Energy-efficient encoding by shifting spikes in neocortical neurons.
    European Journal of Neuroscience , Vol. 38, pp 3181-3188.
  3. Hoch, T., Volgushev, S., Malyshev, A., Obermayer, K. and Volgushev, M. (2011).
    Modulation of the amplitude of $\gamma$-band activity by stimulus phase enhances signal encoding.
    European Journal of Neuroscience, Vol. 33, pp 1223-1239.
  4. Volgushev, M., Malyshev, A., Balaban, P., Chistiakova, M., Volgushev, S., (2008).
    Onset Dynamics of Action Potentials in Rat Neocortical Neurons and Identified Snail Neurons: Quantification of the Difference.
    PLoS ONE 3(4)