Enno MammenProfessor for Mathematical Statistics
Institute for Applied Mathematics
Im Neuenheimer Feld 205
69120 Heidelberg, Germany
Phone: +49 (0) 6221 54 14180 , Fax: +49 (0) 6221 54 14101
- Wednesday 11:00 - 13:00; INF 205/ HS
- Friday 09:00 - 11:00; INF 205 / HS
Seminar Statistics of Dependent Data
- Planned as block seminar
Main seminar Nichtparametrische Statistik
- Thursday 14:00-16:00; INF 205 / SR 7
- Thursday 11:00-13:00; INF 205 / PC-Room Statistics (2nd floor)
Kolloqium für Statistik (with R. Dahlhaus, J. Johannes)
Colloquium of GRK 1953 in Mannheim
Research InterestsAt 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.
Top ten publications in the last ten years
- Linton, O.; Mammen, E., Estimating semiparametric ARCH(∞) models by kernel smoothing methods. Econometrica 73 (2005), 771-836.
- Horowitz, J.; Klemelä, J.; Mammen, E., Optimal estimation in additive regression models. Bernoulli 12 (2006), 271-298.
- Horowitz, J. L.; Mammen, E., Rate-optimal estimation for a general class of nonparametric regression models with unknown link functions. The Annals of Statistics 35 (2007), 2589-2619.
- Hoderlein, S.; Mammen, E., Identification of marginal effects in nonseparable models without monotonicity. Econometrica 75 (2007), 1513-1518.
- Yu, K.; Park, B. U.; Mammen, E., Smooth backfitting in generalized additive models. The Annals of Statistics 36 (2008), 228-260.
- Park, B. U.; Mammen, E.; Härdle, W.; Borak, S., Time series modelling with semiparametric factor dynamics. J. Amer. Statist. Assoc. 104 (2009), 284-298.
- Lee, Y.K.; Mammen, E.; Park, B.U., Flexible generalized varying coefficient regression models. The Annals of Statistics 40 (2012), 1906-1933.
- Mammen, E.; Rothe, C.; Schienle, M., Nonparametric regression with nonparametrically generated covariates. The Annals of Statistics 40 (2012), No. 2, 1132-1170.
- Konakov, V.; Mammen, E.; Woerner, J., Statistical convergence of Markov experiments to diffusion limits. Bernoulli 20 (2014), 623-644.
- Lee, Y.K.; Mammen, E.; Nielsen, J.P.; Park, B.U., Asymptotics for In-Sample Density Forecasting. Ann. Statist. 43 (2015), 620-645.