Eduard Belitser

Office: WN-R346, Phone: +31 20 5987699
Department of Mathematics, VU Amsterdam
De Boelelaan 1081a, 1081 HV Amsterdam

Teaching


Research

The main research interests are in mathematical statistics, in particular:

Publications, preprints, submitted (in reverse chronological order)

  1. Empirical Bayes oracle uncertainty quantification for regression.
    E. Belitser and S. Ghosal. Submitted (2018).
    stat.ncsu.edu/~sghosal/papers/oracle_regression.pdf
  2. Needles and straw in a haystack: robust empirical Bayes confidence for possibly sparse sequences.
    E. Belitser and N. Nurushev. Submitted (2018).
    https://arxiv.org/abs/1511.01803
  3. Local inference by penalization method for biclustering model.
    E. Belitser and N. Nurushev. To appear in Math. Methods Statist, (2018).
  4. Contributed comment on article by Wade and Ghahramani.
    E. Belitser and N. Nurushev. Bayesian Analysis 18, 606-607 (2018).
    https://projecteuclid.org/euclid.ba/1508378464
  5. Contributed comment on article by van der Pas, Szabo, and van der Vaart.
    E. Belitser and N. Nurushev. Bayesian Analysis 12, 1267-1269 (2017).
    https://projecteuclid.org/euclid.ba/1504231319
  6. Local posterior concentration rate for multilevel sparse sequences.
    E. Belitser and N. Nurushev. Bayesian Statistics in Action, Springer Proc. Math. Stat., 194, 51-66 (2017).
    http://www.springer.com/gp/book/9783319540832
  7. On coverage and local radial rates of credible sets.
    E. Belitser. Ann. Stat., 45, 1124-1151 (2017).
    https://doi.org/10.1214/16-AOS1477
  8. Optimal measurement allocation under precision budget constraint.
    E. Belitser. Statist. Probab. Lett., 117, 46-53 (2016).
    https://doi.org/10.1016/j.spl.2016.05.008
  9. Recursive tracking algorithm for a predictable time-varying parameter of a time series.
    E. Belitser and P. Serra. Math. Methods Statist., 24, 243-265 (2015).
    https://doi.org/10.3103/S1066530715040018
  10. Rate-optimal Bayesian intensity smoothing for inhomogeneous Poisson processes.
    E. Belitser, P. Serra and H. van Zanten. J. Statist. Plann. Inference, 166, 24-35 (2015).
    https://doi.org/10.1016/j.jspi.2014.03.009
  11. Recursive estimation of conditional spatial medians and conditional quantiles.
    E. Belitser and P. Serra. Sequential Analysis, 33, 519-538 (2014).
    https://doi.org/10.1080/07474946.2014.961856
  12. Adaptive priors based on splines with random knots.
    E. Belitser and P. Serra. Bayesian Analysis, 9, 859-882 (2014).
    https://doi.org/10.1214/14-BA879
  13. Estimating the period of a cyclic non-homogeneous Poisson process.
    E. Belitser, P. Serra and H. van Zanten. Scand. J. Statist., 40, 204-218 (2013).
    https://doi.org/10.1111/j.1467-9469.2012.00806.x
  14. On properties of the algorithm for pursuing a drifting quantile.
    E. Belitser and P. Serra. Autom. and Rem. Control, 74, 613-627 (2013).
    [https://doi.org/10.1134/S000511791304005X]
  15. Optimal two-stage procedures for estimating location and size of maximum of multivariate regression functions.
    E. Belitser, S. Ghosal and H. van Zanten. Ann. Stat., 40, 2850-2876 (2012).
    https://doi.org/10.1214/12-AOS1053
  16. Oracle Wiener filtering of a Gaussian signal.
    A. Babenko and E. Belitser. Theory Stoch. Process., 17, 16-24 (2011).
    http://tsp.imath.kiev.ua/files/256/tsp1720_2.pdf
  17. Lower bound for the oracle projection posterior convergence rate.
    A. Babenko and E. Belitser. Statist. Probab. Lett., 81, 175-180 (2011).
    https://doi.org/10.3103/S1066530710030026
  18. Oracle convergence rate of posterior under projection prior and Bayesian model selection.
    A. Babenko and E. Belitser. Math. Methods Statist., 19, 219-245 (2010).
    https://doi.org/10.3103/S1066530710030026
  19. On posterior pointwise convergence rate of a Gaussian signal under a conjugate prior.
    A. Babenko and E. Belitser. Statist. Probab. Lett., 79, 670-675 (2009).
    https://doi.org/10.1016/j.spl.2008.10.019
  20. Adaptive filtration of a random signal in Gaussian white noise.
    E. Belitser and F. Enikeeva. Probl. Inf. Transm., 44, 48-60 (2008).
    https://doi.org/10.1134/S0032946008040054
  21. Empirical Bayesian test for the smoothness.
    E. Belitser and F. Enikeeva. Math. Methods Statist., 17, 1-18 (2008).
    https://doi.org/10.3103/S1066530708010018
  22. On asymptotic expansion of pseudovalues in nonparametric median regression.
    E. Belitser. Statistics & Decisions, 22, 1-17 (2004).
    https://doi.org/10.1524/stnd.22.1.1.32715
  23. On the empirical Bayes approach to adaptive filtering.
    E. Belitser and B. Levit. Math. Methods Statist., 12, 131-154 (2003).
    http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.75.5662&rep=rep1&type=pdf
  24. Adaptive Bayesian inference on the mean of an infinite dimensional normal distribution.
    E. Belitser and S. Ghosal. Ann. Stat., 31, 536-559 (2003).
    https://doi.org/10.1214/aos/1051027880
  25. Consistency in nonparametric minimax regression estimation.
    E. Belitser. Comm. in Statistics, 31, 1159-1165 (2002).
    https://doi.org/10.1081/STA-120004914
  26. Minimax recovery of blurred signal from discrete noisy data.
    E. Belitser. J. Nonparametr. Statist., 13, 647-667 (2001).
    https://doi.org/10.1080/10485250108832870
  27. Asymptotically local minimax estimation of infinitely smooth density with censored data.
    E. Belitser and B. Levit. Ann. of Inst. Stat. Math., 53, 289-306 (2001).
    https://doi.org/10.1111/1467-9574.00145
  28. Robust recursive nonparametric curve estimation.
    E. Belitser and S. van de Geer. High Dimensional Probability II,
    E. Gine, D.M. Mason and J.A. Wellner (eds.), 391-404, Birkhauser (2000).
    https://link.springer.com/chapter/10.1007/978-1-4612-1358-1_26
  29. Local minimax pointwise estimation of a multivariate density.
    E. Belitser. Statist. Neerl., 54, 351-365 (2000).
    https://doi.org/10.1111/1467-9574.00145
  30. Recursive estimation of a drifted autoregressive parameter.
    E. Belitser. Ann. Stat., 28, 860-870 (2000).
    https://doi.org/10.1214/aos/1015952001
  31. Minimax estimation in regression and random censorship models.
    E. Belitser. CWI Tract 127, CWI, Amsterdam (2000).
    https://cwilibrary.on.worldcat.org/oclc/906855545
  32. Minimax estimation in the blurred signal model.
    E. Belitser. In: Probability Theory and Mathematical Statistics Series,
    B. Grigelionis, J. Kubilius, V. Paulauskas, H. Pragarauskas, V. Statulevicius (eds.), 57-66, TEV (1999).
    https://books.google.nl/books?isbn=9067643130
  33. Efficient estimation of analytic density under random censorship.
    E. Belitser. Bernoulli, 4, 519-543 (1998).
    https://doi.org/10.2307/3318664
  34. Asymptotically minimax nonparametric regression in L2.
    E. Belitser and B.Y. Levit. Statistics, 28, 105-122 (1996).
    https://doi.org/10.1080/02331889708802553
  35. On minimax filtering over ellipsoids.
    E. Belitser and B.Y. Levit. Math. Methods Statist., 4, 259-273 (1995).
    http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.13.3741
  36. On minimax estimation over ellipsoids.
    E. Belitser and B.Y. Levit. In: Information theory, statistical decision functions, random processes,
    P. Lachout and J.A. Visek (eds.), 28-31, Academy of Sciences of the Czech Republic (1994).
  37. Pseudovalues and minimax filtering algorithms for the nonparametric median.
    E. Belitser and A.P. Korostelev. Adv. in Sov. Math., 12, 115-124 (1992).
  38. On the ergodic properties in dynamical model of stepping devices.
    E. Belitser and A.V. Safonov. In: Methods of applied mathematics for investigating technical systems and mathematical software,
    dep. VINITI, MAI, Moscow (1989).