Page 302 - Special Topic Session (STS) - Volume 1

STS430 Eustasio D.B. et al.
10. B.M. Bolstad, R.A. Irizarry, M. Åstrand, T.P. Speed, A comparison of
normalization methods for high density oligonucleotide array data
based on variance and bias, Bioinformatics 19 (2003) 185–193.
11. V.Chernozhukov, A.Galichon, M.Hallin, M.Henry, Monge–Kantorovich
depth, quantiles, ranks, and signs, Ann.Statist.45(2017)223–256.
12. O. Collier, A.S. Dalalyan, Curve registration by nonparametric goodness-
of-fit testing, J. Statist. Plann. Inf. 162 (2015) 20–42.
13. M. Csörgó, Quantile processes with statistical applications, CBMS-NSF
Regional Conference Series in Applied Mathematics 42, SIAM, 1983.
14. M. Csörgó, L. Horváth, Weighted Approximations in Probability and
Statistics, Wiley, New York, 1993.
15. M.Cuturi, A.Doucet, Fast computation of Wasserstein barycenters,
Proceedings of the International Conference on Machine Learning 2014,
JMLR W&CP 32 (2014) 685–693.
16. C.Czado, A.Munk, Assessing the similarity of distributions–finite sample
performance of the empirical Mallows distance, J.Statist. Comput. Simul.
60 (1998) 319–346.
17. E. del Barrio, P. Deheuvels, S. van de Geer, Lectures on Empirical
Processes: Theory and Statistical Applications, European Mathematical
Society, Zürich, Switzerland, 2007.
18. E. del Barrio, E. Giné, F. Utzet, Asymptotics for L2 functionals of the
empirical quantile process, with applications to tests of fit based on
weighted Wasserstein distances, Bernoulli 11 (2005) 131–189.
19. E. Del Barrio, J.-M.Loubes, Central Limit Theorem for empirical
transportation cost in general dimension. ArXiv e-prints 1705.01299,
2017.
20. J. Ebert, V. Spokoiny, A. Suvorikova, Construction of Non-asymptotic
Confidence Sets in 2-Wasserstein Space ArXiv preprint 1703.03658, 2017

291 | I S I W S C 2 0 1 9