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.