World's Best Scientists 2026 revealed!

D-Index & Metrics

Mathematics

D-Index
51
Citations
12019
World Ranking
1012
National Ranking
52

Overview

Enno Mammen is affiliated with Heidelberg University in Germany and specializes in mathematics with a strong focus on statistics and probability. Their research spans subfields including artificial intelligence, economics and econometrics, management science and operations research, and statistical and nonlinear physics.

The scientist's work centers on topics such as statistical methods and inference, statistical methods and Bayesian inference, advanced statistical methods and models, statistical distribution estimation and applications, control systems and identification, spatial and panel data analysis, and financial risk and volatility modeling.

Enno Mammen has published extensively across multiple venues, contributing notably to arXiv (Cornell University), Biometrika, Computational Statistics & Data Analysis, The Annals of Statistics, and Bernoulli.

  • A nested copula duration model for competing risks with multiple spells, 2020, Computational Statistics & Data Analysis
  • Nonparametric regression with parametric help, 2020, Electronic Journal of Statistics
  • Poisson reduced-rank models with an application to political text data, 2020, Biometrika
  • Locally polynomial Hilbertian additive regression, 2022, Bernoulli
  • Smooth Backfitting of Proportional Hazards With Multiplicative Components, 2020, Journal of the American Statistical Association

Frequent collaborators include Jens Perch Nielsen, Munir Hiabu, Joseph T. Meyer, Byeong U. Park, and María Dolores Martínez-Miranda.

In addition to journal articles, Enno Mammen has contributed to book literature, with the publication of a title through Springer International Publishing: Foundations of Modern Statistics (2023).

Best Publications

  • Comparing Nonparametric Versus Parametric Regression Fits

    W. Hardle;E. Mammen

  • Bootstrap and Wild Bootstrap for High Dimensional Linear Models

    Enno Mammen

  • Smooth Discrimination Analysis

    Enno Mammen;Alexandre B. Tsybakov

  • The Existence and Asymptotic Properties of a Backfitting Projection Algorithm under Weak Conditions

    Enno Mammen;Oliver Linton;J Nielsen

  • Locally adaptive regression splines

    Enno Mammen;Sara van de Geer

  • Optimal spatial adaptation to inhomogeneous smoothness: an approach based on kernel estimates with variable bandwidth selectors

    O. V. Lepski;E. Mammen;V. G. Spokoiny

  • Estimating a Smooth Monotone Regression Function

    Enno Mammen

  • On estimation of monotone and concave frontier functions

    Irène Gijbels;Enno Mammen;Byeong U. Park;Léopold Simar

  • When Does Bootstrap Work?: Asymptotic Results and Simulations

    Enno Mammen

  • Nonparametric regression under qualitative smoothness assumptions

    Enno Mammen

  • Direct estimation of low-dimensional components in additive models

    Jianqing Fan;Wolfgang Härdle;Enno Mammen

  • A General Projection Framework for Constrained Smoothing

    E. Mammen;J.S. Marron;Berwin Turlach;M.P. Wand

  • Nonparametric estimation of an additive model with a link function

    Joel L. Horowitz;Enno Mammen

  • Penalized quasi-likelihood estimation in partial linear models

    Enno Mammen;Sara van de Geer

  • Estimating Semiparametric ARCH(∞) Models by Kernel Smoothing Methods

    Oliver Linton;Enno Mammen

  • Testing Parametric Versus Semiparametric Modeling in Generalized Linear Models

    Wolfgang Härdle;Enno Mammen;Marlene Müller

  • Asymptotical minimax recovery of sets with smooth boundaries

    Enno Mammen;Alexandre B. Tsybakov

  • Bootstrap of kernel smoothing in nonlinear time series

    Jürgen Franke;Jens-Peter Kreiss;Enno Mammen

  • Thresholding algorithms, maxisets and well-concentrated bases

    Gérard Kerkyacharian;Gérard Kerkyacharian;Dominique Picard;Lucien Birgé;Peter Hall

  • A semiparametric factor model for implied volatility surface dynamics

    Matthias R. Fengler;Wolfgang K. Härdle;Enno Mammen

  • Identification of marginal effects in nonseparable models without monotonicity

    Stefan Hoderlein;Enno Mammen

  • The Bootstrap and Edgeworth Expansion

    Enno Mammen

Frequent Co-Authors

Oliver Linton
Oliver Linton University of Cambridge
Byeong U. Park
Byeong U. Park Seoul National University
Wolfgang Karl Härdle
Wolfgang Karl Härdle Humboldt-Universität zu Berlin
Raymond J. Carroll
Raymond J. Carroll Texas A&M University
James Stephen Marron
James Stephen Marron University of North Carolina at Chapel Hill
Alexandre B. Tsybakov
Alexandre B. Tsybakov École Nationale de la Statistique et de l'Administration Économique
Gerard J. van den Berg
Gerard J. van den Berg University of Groningen
Xihong Lin
Xihong Lin Harvard University

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

For students interested in pursuing Mathematics in the USA, exploring related online degrees can broaden career opportunities. Many professionals complement their math background with business-focused programs such as an easiest mba to get into, which offers accessible pathways into management roles.

Additionally, for those seeking flexible study options, the easiest online mba programs provide a convenient way to combine analytical skills gained in math with strategic business knowledge. This can help open doors in consulting, finance, or data analysis careers.

For advanced professionals aiming to deepen their expertise, pursuing the cheapest aacsb online dba programs offers a cost-effective route to doctoral studies that blend quantitative research with organizational leadership.

Finally, given the strong ties between mathematics and finance, enrolling in a cheap online masters in finance can be an excellent way to specialize in financial modeling, investment analysis, or risk management—all of which leverage mathematical skills in practical, high-demand fields.

Best Scientists Citing Enno Mammen

Trending Scientists

Recently Published Articles