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Publikationen



Publikationen in referierten Zeitschriften

  1. Neumann, M. H. (1992). Second order asymptotic risks of smoothed linear estimators in nonparametric regression models. Statistics 23, 217-236.
  2. Neumann, M. H. (1994). Fully data-driven nonparametric variance estimators. Statistics 25, 189-212.
  3. Neumann, M. H. (1995). Automatic bandwidth choice and confidence intervals in nonparametric regression. Annals of Statistics 23, 1937-1959.
  4. Neumann, M. H. and Spokoiny, V. G. (1995). On the efficiency of wavelet estimators under arbitrary error distributions. Mathematical Methods of Statistics 4, 137-166.
  5. Neumann, M. H. (1995). Discussion to the paper "Wavelet shrinkage: asymptopia?" by Donoho et al., Journal of the Royal Statistical Society Ser. B 57, 346-347.
  6. Neumann, M. H. and von Sachs, R. (1995). Wavelet thresholding: beyond the Gaussian i.i.d. situation. In Lecture Notes in Statistics: Wavelets and Statistics , A. Antoniadis ed., 301-329.
  7. Neumann, M. H. (1996). Spectral density estimation via nonlinear wavelet methods for stationary non-Gaussian time series. Journal of Time Series Analysis 17, 601-633.
  8. Neumann, M. H. and von Sachs, R. (1997). Wavelet thresholding in anisotropic function classes and application to adaptive estimation of evolutionary spectra. Annals of Statistics 25, 38-76.
  9. Hall, P., Marron, J. S., Neumann, M. H. and Titterington, D. M. (1997). Curve estimation when the design density is low. Annals of Statistics 25, 756-770.
  10. Neumann, M. H. (1997). On the effect of estimating the error density in nonparametric deconvolution. Journal of Nonparametric Statistics 7, 307-330.
  11. Neumann, M. H. (1997). Optimal change-point estimation in inverse problems. Scandinavian Journal of Statistics 24, 503-521.
  12. Neumann, M. H. (1997). Pointwise confidence intervals in nonparametric regression with heteroscedastic error structure. Statistics 29, 1-36.
  13. Neumann, M. H. and Kreiss, J.-P. (1998). Regression-type inference in nonparametric autoregression. Annals of Statistics 26, 1570-1613.
  14. Neumann, M. H. (1998). Strong approximation of density estimators from weakly dependent observations by density estimators from independent observations. Annals of Statistics 26, 2014-2048.
  15. Marron, J. S., Adak, S., Johnstone, I. M., Neumann, M. H. and Patil, P. (1998). Exact risk analysis of wavelet regression. Journal of Graphical and Computational Statistics 7, 278-309.
  16. Klinke, S., Golubev, Yu., Härdle, W. and Neumann, M. H. (1998). Teaching wavelets in Xplore. Computational Statistics 13. 141-151.
  17. Neumann, M. H. and Polzehl, J. (1998). Simultaneous bootstrap confidence bands in nonparametric regression. Journal of Nonparametric Statistics 9, 307-333.
  18. Dahlhaus, R., Neumann, M. H. and von Sachs, R. (1999). Nonlinear wavelet estimation of time-varying autoregressive processes. Bernoulli 5, 873-906.
  19. Neumann, M. H. and Paparoditis, E. (2000). On bootstrapping L2 -type statistics in density testing. Statistics and Probabability Letters 50, 137-147.
  20. Neumann, M. H. (2000). Multivariate wavelet thresholding in anisotropic function spaces. Statistica Sinica 10, 399-431.
  21. Benkwitz, A., Lütkepohl, H. and Neumann, M. H. (2000). Problems related to confidence intervals for impulse responses of autoregressive processes. Econometric Reviews 19, 69-103.
  22. Franke, J. and Neumann, M. H. (2000). Bootstrapping neural networks. Neural Computation 12, 1929-1949.
  23. von Sachs, R. and Neumann, M. H. (2000). A wavelet-based test for stationarity. Journal of Time Series Analysis 21, 597-613.
  24. Dahlhaus, R. and Neumann, M. H. (2001). Locally adaptive fitting of semiparametric models to nonstationary time series. Stochastic Processes and Applications 91, 277-308.
  25. Neumann, M. H. (2001). On robustness of model-based bootstrap schemes in nonparametric time series analysis. Statistics 35, 1-40.
  26. Franke, J., Kreiss, J.-P., Mammen, E. and Neumann, M. H. (2002). Properties of the nonparametric autoregressive bootstrap. Journal of Time Series Analysis 23, 555-585.
  27. Franke, J., Neumann, M. H. and Stockis, J.-P. (2004). Bootstrapping nonparametric estimators of the volatility function. Journal of Econometrics 118, 189-218.
  28. Herwartz, H. and Neumann, M. H. (2005). Bootstrap Inference in Systems of Single Equation Error Correction Models. Journal of Econometrics 128, 165-193.
  29. Butucea, C. and Neumann, M. H. (2005). Exact asymptotics for estimating the marginal density of discretely observed diffusion processes. Bernoulli 11, 411-444.
  30. Kallabis, R. S. and Neumann, M. H. (2006). An exponential inequality under weak dependence. Bernoulli 12, 333-350.
  31. Grama, I. G. and Neumann, M. H. (2006). Asymptotic equivalence of nonparametric autoregression and nonparametric regression. Annals of Statistics 34, 1701-1732.
  32. Neumann, M. H. and Thorarinsdottir, T. L. (2006). Minimax estimation in nonparametric autoregression, Mathematical Methods of Statistics 15, 374-397.
  33. Doukhan, P. and Neumann, M. H. (2007). Probability and moment inequalities for sums of weakly dependent random variables, with applications, Stochastic Processes and Their Applications 117, 878-903.
  34. Neumann, M. H. (2007). Deconvolution from panel data with unknown error distribution. Journal of Multivariate Analysis 98, 1955-1968.
  35. Neumann, M. H. and Paparoditis, E. (2007). Goodness-of-fit tests for Markovian time series models: Central limit theory and bootstrap. Bernoulli 14, 14-46.
  36. Doukhan, P. and Neumann, M. H. (2008). The notion of ψ-weak dependence and its applications to bootstrapping time series. Probability Surveys 5, 146-168.
  37. Neumann, M. H. and Paparoditis, E. (2008). Simultaneous confidence bands in spectral density estimation. Biometrika 95, 381-397.
  38. Kreiß, J.-P., Neumann, M. H. and Yao, Q. (2008). Bootstrap tests for simple structures in nonparametric time series regression. Statistics and Its Interface 1, 367-380.
  39. Neumann, M. H. and Reiß, M. (2008). Nonparametric estimation for Lévy processes from low-frequency observations. Bernoulli 15, 223-248.
  40. Leucht, A. and Neumann, M. H. (2009). Consistency of general bootstrap methods for degenerate U- and V-type statistics. Journal of Multivariate Analysis 100, 1622-1633.
  41. Meister, A. and Neumann, M. H. (2009). Deconvolution from non-standard error densities under replicated measurements. Statistica Sinica 20, 1609-1636.
  42. Neumann, M. H. (2011). Absolute regularity and ergodicity of Poisson count processes. Bernoulli 17. 1268-1284.
  43. Leucht, A. and Neumann, M. H. (2013). Degenerate U- and V-statistics under ergodicity: asymptotics, bootstrap and applications in statistics. Annals of the Institute of Statistical Mathematics 65 (2), 349-386.
  44. Neumann, M. H. (2013). A central limit theorem for triangular arrays of weakly dependent random variables, with applications in statistics. ESAIM: Probability and Statistics 17, 120-134.
  45. Leucht, A. and Neumann, M. H. (2013). Dependent wild bootstrap for degenerate U- and V-statistics. Journal of Multivariate Analysis 117, 257-280.
  46. Fokianos, K. and Neumann, M. H. (2013). A goodness-of-fit test for Poisson count processes. Electronic Journal of Statistics 7, 793-819.
  47. Doukhan, P., Lang, G., Leucht, A. and Neumann, M. H. (2015). Dependent wild bootstrap for the empirical process. Journal of Time Series Analysis 36, 290-314.
  48. Leucht, A., Neumann, M. H. and Kreiss, J.-P. (2015). A model specification test for GARCH(1,1) processes. Scandinavian Journal of Statistics 42, 1167-1193.


Andere Publikationen

  • Neumann, M. H. (1990). Asymptotic results for kernel estimators of the mean vector and the heteroscedastic variance vector without replications at the design points. Seminarbericht No. 109, Humboldt University. (ein Teil ist unter dem Titel "Fully data-driven nonparametric variance estimators" erschienen in Statistics 25, 189-212, 1994)
  • Neumann, M. H. (1991). On the asymptotic efficiency of kernel estimators of regression. Preprint No. 91-21, Humboldt University.
  • Neumann, M. H. (1999). Discussion to Booth, Bühlmann and Wood. Proceedings of the 52nd Session of the International Statistical Institute, Helsinki 1999, 97-98.
  • Herwartz, H. and Neumann, M. H. (2006). A robust bootstrap approach to the Hausman test in stationary panel data models, Preprint.
  • Fokianos, K., Leucht, A. and Neumann, M. H. (2016). Multivariate isotonic time series regression. Manuskript.

Abschlußarbeiten

Diplomarbeit

Asymptotische Güteaussagen zweiter Ordnung für adaptive
Schätzungen des Erwartungswertvektors,
Betreuer Prof. Dr. O. Bunke, 1988



Dissertation

Asymptotische Untersuchungen der Güte von nichtparametrischen Schätzungen des Erwartungswert- und Varianzvektors,
Betreuer Prof. Dr. O. Bunke, 1991


Habilitationsschrift

Approximationen und nichtparametrische Methoden in der Zeitreihenanalyse, 1999
(kumulativ, basierend auf [7], [8], [13], [14], [18], [20], mit 55-seitiger Zusammenfassung)

Wissenschaftliche Vorträge

  • Joint Seminar on Regression Analysis and Biometrics, INRA Paris - Humboldt-Universität zu Berlin : Asymptotic results for kernel estimators of the mean and of heteroscedastic variances (September 1990)
  • 7th European Young Statisticians Meeting, Oberwolfach: Completely data-driven kernel estimators of the variance (August 1991)
  • INRA, Jouy-en-Josas, Frankreich: A completely data-driven kernel estimator of the variance (Oktober 1991)
  • INRA, Jouy-en-Josas, Frankreich: Completely data-driven pointwise confidence intervals in nonparametric regression (März 1992)
  • Konferenz "Curves, Images, Massive Computation", Oberwolfach: The minimax property of wavelet shrinkage under arbitrary error distributions (März 1993)
  • Universität Rostock: Wavelet estimators in statistics (Juni 1993)
  • Fifth Prague Symposium on Asymptotic Statistics, Prag: On the efficiency of wavelet estimators under arbitrary error distributions (September 1993)
  • Humboldt-Universität zu Berlin, Institut für Ökonometrie und Statistik: Wavelet estimators in statistics (Oktober 1993)
  • Konferenz "Mathematische Stochastik", Oberwolfach: Wavelet methods in non-Gaussian models (März 1994)
  • 3rd World Congress of The Bernoulli Society and 57th Annual Meeting of The Institute of Mathematical Statistics, Chapel Hill , USA : Bootstrap confidence bands in nonparametric regression (Juni 1994)
  • Workshop on New Directions in Curve Estimation, ANU, Canberra : Wavelet methods in spectral density estimation (Juli 1994)
  • University of New South Wales , Sydney : Wavelet methods in non-Gaussian regression and spectral density estimation (Juli 1994)
  • Australian National University , Canberra : Bootstrap confidence bands in nonparametric regression (August 1994)
  • Seminar Berlin-Paris, Garchy, Frankreich: Wavelet methods in non-Gaussian regression and spectral density estimation (September 1994)
  • XVth Franco-Belgian meeting "Wavelets and Statistics", Villard de Lans, Frankreich: Spectral density estimation for nonstationary processes (November 1994)
  • 19. Jahrestagung der Gesellschaft für Klassifikation, Basel: Nonlinear wavelet methods in curve estimation (März 1995)
  • Ruprecht-Karls-Universität Heidelberg: Spectral density estimation for locally stationary time series (Juli 1995)
  • Ruprecht-Karls-Universität Heidelberg: Optimal change-point estimation in inverse problems (Juli 1995)
  • 21st European Meeting of Statisticians, Aarhus : Optimal change-point estimation in inverse problems (eingeladener Vortrag, August 1995)
  • Statistische Woche, Leipzig: Nichtlineare Waveletmethoden in der Kurvenschätzung (September 1995)
  • Seminar Berlin-Paris, Schmerwitz: Optimal change-point estimation in inverse problems (September 1995)
  • Conference "Smoothing and Resampling in Economics", HU Berlin: Wavelet thresholding in anisotropic function classes and applications to adaptive estimation of evolutionary spectra (Oktober 1995)
  • Technische Universität Braunschweig: Nichtlineare Waveletschätzung von zeitveränderlichen autoregressiven Prozessen (November 1995)
  • Freiberger Stochastiktage: Bootstrap-Konfidenzbänder in der nichtparametrischen Regression (März 1996)
  • Fourth World Congress of the Bernoulli Society, Wien: Multivariate wavelet thresholding: a remedy against the curse of dimensionality? (August 1996)
  • Symposium on Operations Research 96, TU Braunschweig: Bootstrap confidence bands in nonparametric autoregression (September 1996)
  • Seminar Paris-Berlin, Garchy: Strong approximation and bootstrap in nonparametric autoregression (Oktober 1996)
  • Université catholique de Louvain , Louvain-la-Neuve, Belgien: Strong approximations in time series models (Dezember 1996)
  • SFB 373, Berlin: Starke Approximationen in Zeitreihenmodellen (Dezember 1996)
  • Universität Osnabrück: Waveletmethoden in der nichtparametrischen Kurvenschätzung (Dezember 1996)
  • Universität Kaiserslautern: Das Bootstrap-Prinzip und Anwendungen in der Zeitreihenanalyse (Dezember 1996)
  • Workshop "The Art of Nonparametric Statistics: Methodologies and Applications", Louvain-la-Neuve, Belgien: Strong approximation of density estimators from weakly dependent observations by density estimators from independent observations (Poster, Februar 1997)
  • ISI-Satellite Meeting, Rostock : Bootstrapping nonparametric estimators of the autoregression function (September 1997)
  • Seminar Berlin-Paris, Schmerwitz: Strong approximations and bootstrap for nonparametric estimators from dependent rv's (September 1997)
  • Statistische Woche, Bielefeld: Bootstrap in der nichtparametrischen Zeitreihenanalyse (September 1997)
  • University of Cyprus , Nicosia : Wavelets and Statistics (Oktober 1997)
  • Konferenz "Mathematical Stochastics", Oberwolfach: Equivalence of nonparametric estimators under weak dependence and independence (März 1998)
  • Münchner Stochastiktage: Nichtparametrische Vorhersage bei allgemeinen stochastischen Prozessen (März 1998)
  • Ruprecht-Karls-Universität Heidelberg: I.i.d.-typ Bootstrap für nichtparametrische Schätzer bei Zeitreihen (Mai 1998)
  • Otto-von-Guericke-Universität Magdeburg: Bootstrap-Methoden in der nichtparametrischen Zeitreihenanalyse (Juni 1998)
  • Workshop "Empirical processes in non- and semiparametric statistics", Humboldt-Universität zu Berlin : Regression-type inference in nonparametric autoregression (August 1998)
  • École Polytechnique Fédérale de Lausanne: Strong approximations and bootstrap for nonparametric estimators in time series analysis (November 1998)
  • Eidgenössische Technische Hochschule Zürich: Bootstrap für nichtparametrische Schätzer bei Zeitreihen (November 1998)
  • Universität Potsdam: Wavelets in der nichtparametrischen Kurvenschätzung (Januar 1999)
  • Freie Universität Berlin, Quantitativ Ökonomisches Colloquium: Bootstrap in der nichtparametrischen Zeitreihenanalyse (Februar 1999)
  • Université catholique de Louvain , Louvain-la-Neuve, Belgien: Exact asymptotics for density estimation from dependent data (Mai 1999)
  • Universität Kaiserslautern: "Exakte Asymptotik" für Dichteschätzer bei abhängigen Daten (Mai 1999)
  • 52nd Session of the ISI, Helsinki : Invited discussant in the section "Advances in Resampling Methods" (August 1999)
  • Jahrestagung der DMV, Mainz: Nichtparametrische Kurvenschätzung bei abhängigen Daten (eingeladener Vortrag, September 1999)
  • Universität Karlsruhe (TH): Starke Approximationen in der nichtparametrischen Kurvenschätzung (Januar 2000)
  • Universität zu Köln: Bootstrap und starke Approximationen (Januar 2000)
  • Hamburger Stochastiktage: Wavelets in nonparametric curve estimation (eingeladener Vortrag, März 2000)
  • Centre de Recherche en Economie et Statistique, Paris: Bootstrap and strong approximations (April 2000)
  • Université Paris X: Bootstrap and strong approximations (April 2000)
  • 5th World Congress of the Bernoulli Society and 63rd Annual Meeting of the IMS, Guanajuato , Mexico : Nonparametric tests in time series analysis (eingeladener Vortrag, Mai 2000)
  • Universität Bayreuth: Bootstrap und starke Approximationen (Juli 2000)
  • Festkolloquium zum 65. Geburtstag von Prof. Dr. O. Bunke, Humboldt-Universität zu Berlin: Nichtparametrische Kurvenschätzung für abhängige Daten (Oktober 2000)
  • Technische Universität Braunschweig: Nichtparametrische Methoden in der Finanzmathematik (Oktober 2000)
  • Ruhr-Universität Bochum: "Exakte" Asymptotik in der nichtparametrischen Dichteschätzung bei abhängigen Daten (Dezember 2000)
  • Georg-August-Universität Göttingen: Nichtparametrische Kurvenschätzung bei Zeitreihen (Februar 2001)
  • Universidade da Coruña: Efficient nonparametric estimation for dependent data (März 2001)
  • Universidade de Santiago de Compostela: Testing the transition mechanism of times series models (März 2001)
  • Workshop "New Directions in Time Series Analysis", CIRM, Luminy: Testing the transition mechanism of times series models (April 2001)
  • Westfälische Wilhelms-Universität Münster: Aktuelle Probleme in der nichtparametrischen Kurvenschätzung (Januar 2002)
  • Universidad Carlos III de Madrid: Testing the transition mechanism of times series models (Januar 2002)
  • Université catholique de Louvain , Louvain-la-Neuve, Belgien: Tests for time series models (Februar 2002)
  • Magdeburger Stochastiktage: Tests for time series models (März 2002)
  • International Conference on Current Advances and Trends in Nonparametric Statistics, Kreta: Tests of time series models (Juli 2002)
  • Université Paris X: Goodness-of-fit tests for Markovian time series models (März 2003)
  • Université Paris X: Nonparametric prediction with high-dimensional explanatory variables and some basic tools under weak dependence (März 2003)
  • Universität Rostock: "Weak dependence" statt "mixing" - Grundlegende Resultate und mögliche Anwendungen in der Statistik (Juni 2003)
  • Universität Hamburg: "Weak dependence" statt "mixing" - Grundlegende Resultate und möögliche Anwendungen in der Statistik (Oktober 2003)
  • Workshop "New directions in Time Series Analysis", CIRM, Luminy: Some basic tools under weak dependence (Dezember 2003)
  • Universität Göttingen: "Weak dependence" statt "mixing" - Beispiele und grundlegende Resultate (Januar 2004)
  • Friedrich-Schiller-Universität Jena: Nichtparametrische Tests von Zeitreihenmodellen (März 2004)
  • Karlsruher Stochastiktage: Doukhan's concept of weak dependence - examples and basic tools (März 2004)
  • Workshop "New directions in Time Series Analysis", Protaras (Zypern): Doukhan's concept of weak dependence - examples and basic tools (Juni 2004)
  • Workshop "Non-Linear Time Series Modeling", Kopenhagen: Doukhan's concept of weak dependence - examples and basic tools (Oktober 2004)
  • Weierstraß-Institut, Berlin: Anpassungstests für Zeitreihenmodelle (Dezember 2004)
  • Workshop "Nonlinear and nonstationary time series", Universität Kaiserslautern: The concept of weak dependence - ideas and tools (September 2005)
  • ENSAE Paris : Deconvolution from panel data with unknown error distribution (Oktober 2005)
  • ESF SCSS Exploratory Workshop "Specification testing", Santander: Goodness-of-fit tests for Markovian time series models (Dezember 2005)
  • Frankfurter Stochastiktage: Deconvolution from panel data with unknown error distribution (März 2006)
  • 26th European Meeting of Statisticians, Torum: The notion of weak dependence and tools for application in statistics (eingeladener Vortrag, Juli 2006)
  • 10th Latin American Congress of Probability and Mathematical Statistics, Lima : Probability and moment inequalities for sums of weakly dependent random variables (eingeladener Vortrag, Februar 2007)
  • Tagung "Semiparametric and Nonparametric Methods in Econometrics", Oberwolfach: Weak dependence vs. mixing -- basic ideas and tools (März 2007)
  • Universität Dortmund: "Weak dependence" - eine Alternative zu Mischungskonzepten (Juni 2007)
  • Aachener Stochastiktage: Nonparametric estimation for Lévy processes from low-frequency observations (März 2008)
  • Workshop "Time Series and Bootstrap", Kaiserslautern: Nonparametric estimation for Lévy processes from low-frequency observations (Juni 2008)
  • Tagung "Statistics for Dependent Data", Paris: Nonparametric estimation for Lévy processes from low-frequency observations (Juni 2008)
  • International Workshop on Recent Advances in Time Series Analysis, Protaras (Zypern): Nonparametric estimation for Lévy processes from low-frequency observations
    (Juni 2008)
  • University of Cyprus, Nicosia: Dependence in probability and statistics (September 2008)
  • Deutsch-Rumänisches Symposium, Sibiu: Wek dependence from a statistician's perspective (eingeladener Vortrag, Mai 2009)
  • Universität Potsdam: Bootstrap-Verfahren für Zeitreihen (Juli 2009)
  • Workshop "Limit Theory and Statistics of Time Series", Cergy: A nonstationary CLT, with applications (eingeladener Vortrag, Januar 2010)
  • Leipziger Stochastiktage: Poisson count processes: Ergodicity and goodness-of-fit (März 2010)
  • Conference on Resampling Methods and High Dimensional Data 2010, College Station: Weak dependence from a statistician's perspective (eingeladener Vortrag, März 2010)
  • Universität Hamburg: "Wek dependence" - eine Alternative zu Mischungskonzepten (Juli 2010)
  • Ruprecht-Karls-Universität Heidelberg: Zeitreihen für Zähldaten: Ergodizität und ein Anpassungstest (Juli 2010)
  • 28th European Meeting of Statisticians, Piräus: Poisson count processes: Ergodicity and goodness-of-fit (August 2010)
  • 2nd International Workshop on Integer Valued Time Series, Protaras (Zypern): Goodness-of-fit for Poisson count processes: A V-Statistics approach. (Juni 2011)
  • 2nd NTH Workshop on Finance and Insurance Mathematics: Cramér-von Mises-type tests: A V-statistics approach (eingeladener Vortrag, Juli 2011)
  • Université Cergy-Pontoise: Goodness-of-fit for Poisson count processes: A V-statistics approach (Januar 2012)
  • TU Braunschweig: Bootstrap für degenerierte U- und V-Statistiken (Januar 2012)
  • Workshop "Prediction of Time Series and Nonstationary Time Series", Paris: Dependent wild bootstrap for degenerate U- and V-statistics (Februar 2012)
  • Mainzer Stochastiktage: Goodness-of-fit for Poisson count processes: A V-statistics approach (März 2012)
  • Workshop "Statistics of Lévy-driven Models", Universität Ulm: Dependent wild bootstrap for degenerate U- and V-statistics (eingeladener Vortrag, März 2012)
  • Interantional Workshop on Recent Advances in Time Series Analysis, Protaras; Zypern: Bootstrap for degenerate U- and V-statistics (eingeladener Vortrag, Juni 2012)
  • 1st Conference of the International Society for NonParametric Statistics, Chalkidiki (Griechenland): Asymptotics and bootstrap consistency for degenerate von Mises-statistics of ergodic processes (eingeladener Vortrag, Juni 2012)
  • Workshop "Nichtparametrische und nichtlineare Zeitreihenanalyse", Lambrecht: Asymptotics and bootstrap consistency for degenerate von Mises-statistics of ergodic-processes (eingeladener Vortrag, September 2012)
  • DMV-Jahrestagung, Saarbrücken: Bootstrap for degenerate U- and V-statistics of dependent random variables (September 2012)
  • Universität Mannheim: Dependent wild bootstrap (November 2012)