Estimation of Quantile and other Nonparametric Statistical Functionals in Non- and Semiparametric Regression and Autoregression Models

• Antragsteller: Professor Dr. Enno Mammen
• Fachliche Zuordnung: Mathematik
• Förderung: Förderung von 2006 bis 2011
• Projektkennung: Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 21235440

• Erstellungsjahr: 2011

Zusammenfassung der Projektergebnisse

1. Higher dimensional nonparametric models with several nonparametric components have been analysed in different dependence structures, including nonstationarity and local stationarity. 2. New GARCH-models have been developed that include nonparametric components in their dynamics. 3. In the setting of density estimation estimation and visualization methods were developed to cope with high dimensional cases. An asymptotic theory has been developed for their performance. 4. Higher dimensional nonparametric quantile models have been studied and an asymptotic theory for backfitting estimators has been developed.

Projektbezogene Publikationen (Auswahl)

• (2009). L2 boosting in kernel regression. Bernoulli 15, 599-613
  B.U. Park, Y. K. Lee and S. Ha
  
• (2009). Multivariate histograms with data-dependent partitions. Statistica Sinica 19, 159-176
  Klemelä, J.
  
• (2009). Smoothing of Multivariate Data: Density Estimation and Visualization. Wiley, New York
  Klemelä, J.
  
• (2010). Nonparametric Estimation of Distributional Policy Effects. Journal of Econometrics 155, 56-70
  Rothe, C.