Last edited: 2019-06-05 by dn.
Siegel der Universität Heidelberg

Enno Mammen

Professor for Mathematical Statistics Enno Mammen

Postal address

Heidelberg University
Institute for Applied Mathematics
MΛTHEMΛTIKON
Im Neuenheimer Feld 205
69120 Heidelberg, Germany
Phone: +49 (0) 6221 54 14180 , Fax: +49 (0) 6221 54 14101
E-mail: mammen@math.uni-heidelberg.de


Teaching SS 2019

Lecture

Höhere Mathematik für Physiker II
Wednesday 9:00 - 11:00; Friday 11:00 - 13:00, INF 227 / HS 1; with practice classes

Plenarübung zur Höheren Mathematik für Physiker II
Wednesday 16:00 - 18:00 weekly, INF 227 / HS 1.

More information:
https://nps.math.uni-heidelberg.de/HM2/.

Main seminar

Nichtparametrische Statistik
Tuesday 11:00 - 13:00, INF 205 / SR 5.

Colloquia

Kolloqium für Statistik (with R. Dahlhaus, J. Johannes), Thursday 14 - 16, INF 205, SR 7.

Colloquium of GRK 1953 (Heidelberg/Mannheim).

Research interests

At the start of my career I worked on asymptotic statistical decision theory (LeCam-Theory). In my thesis I solved a question raised by Lucien LeCam on the information contained in additional observations, published in an Annals of Statistics paper in 1989. Afterwards I studied parametric models with increasing dimension and nonparametric curve estimation problems. In particular, I looked on the performance of bootstrap methods in these models. In an influential paper in the Annals of Statistics from 1993, joint with Wolfgang Härdle, I proposed the wild bootstrap procedure for nonparametric regression problems and I studied wild bootstrap in another Annals paper of 1993 for high-dimensional linear models. Other work on bootstrap is contained in a Springer Lecture Notes in 1992. Further interests were nonparametric curve estimation under shape constraints (e.g. monotonicity or convexity), published among others in two Annals of Statistics papers in 1991. Furthermore I showed in an Annals paper from 1997 with O. Lepski and V. Spokoiny that one can achieve the same rates of convergence in Besov spaces by kernel smoothing compared with wavelet-estimators if one uses data-adaptive local bandwidths chosen with the Lepski-rule. With S. van de Geer I applied empirical process methods to study nonparametric estimators based on penalization. We wrote two Annals of Statistics papers in 1997. In one paper we used an L1-penalty and showed that this leads to a sparse number of jumps of a function or its derivative, respectively. With A.B. Tsybakov I worked on estimation of sets in classification and discrimination. We summarized our research in two Annals papers (1995, 1999). Starting with Annals of Statistics in 1999, joint with O. Linton and J.P. Nielsen, on additive models I looked at nonparametric models with several nonparametric components. In a series of papers I worked on this topic, in particular together with Oliver Linton, Cambridge; Joel Horowitz, Northwestern, Jens-Perch Nielsen, London, and Byeong Park, Seoul. Another ongoing more probabilistic interest, joint with Valentin Konakov, Moscow, are higher order limit statements of the convergence of Markov processes to diffusions.

Education and career


Top ten publications in the last ten years

  1. Small time Edgeworth-type expansions for weakly convergent nonhomogenous Markov chains. Prob. Theory and Rel. Fields, 143, 137-176 (with V. Konakov)
  2. Empirical risk minimization in inverse problems. Ann. Statist. 38, 482-511 (with J. Klemelä)
  3. Nonparametric regression with filtered data. Bernoulli 17, 60-87 (with O. Linton, J. P. Nielsen and I. van Keilegom)
  4. Do-validation with kernel density estimation. J. Amer. Stat. Assoc. 106(494): 651-660 (with M. D. Martínez Miranda, J. P. Nielsen and S. Sperlich )
  5. Flexible generalized varying coefficient regression models. Ann. Statist. 40, 1906-1933 (with Y. K. Lee and B. U. Park)
  6. Nonparametric regression with nonparametrically generated covariates. Ann. Statist. 40, 1132-1170 (with C. Rothe and M. Schienle)
  7. Statistical convergence of Markov experiments to diffusion limits. Bernoulli 20, 623-644 (with V. Konakov and J. Woerner)
  8. In sample forecasting with local linear survival densities. Biometrika, 103, 843-859 (with M. Hiabu, M. D. Martínez Miranda, and J. P. Nielsen)
  9. Operational time and in-sample density forecasting. Ann. Statist., 45, 1312 - 1341 (with Y. K. Lee, J. P. Nielsen and B. U. Park)
  10. Expansions for moments of regression quantiles with applications to nonparametric testing. Bernoulli 25(2), 793-827 (with I. van Keilegom and K. Yu).
→ For all publications see: Publications and preprints, discussion of papers, book reviews

Major Contributions to early careeers of young researchers.

In the last ten years 11 students have finished a PhD thesis under my supervision. Three of my former PhD students are now full professors, two associate professors and three assistant professors. Topics of the PhD thesis include long memory GARCH models, nonparametric GARCH in mean models, additive nonparametric diffusion models, nonparametric regression tests, additive regression models with nonstationary covariates, nonparametric microeconometrics, local stationary nonparametric regression models, nonparametric instrumental regression, and high-dimensional nonparametric models. Currently I have 3 further PhD students, working on nonparametric Hawkes processes, on statistical theory of neural networks, and statistics of non-Euclidean data. I am co-supervising two PhD-students working on nonparametric survival analysis and in-sample forecasting.
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