Stanislav Volgushev

Stanislav Volgushev
Stanislav
Volgushev
Assistant Professor

The main aim of my research is to develop tractable statistical methods that can capture distributional effects which are not described by classical 'mean-based' analysis. So far, my research in this direction has focused on quantile regression and copulas. Currently, I am also working on several projects that deal with computational and statistical aspects of estimation and inference in extremely large data sets.

Some recent publications are as follows:

Kley, T., Volgushev, S., Dette, H. and Hallin, M. (2015+). Quantile spectral processes: Asymptotic analysis and inference. Bernoulli, to appear.

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.

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

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.

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.