Teaching

Book

Preprints

Software

Publications

Animations

Talks

Jussi Klemelä

Researcher, Dos., Dr.
Contact info

Jussi Klemelä
University of Oulu
Department of Mathematical Sciences
P.O. Box 3000
90014 University of Oulu, Finland

Email: jussi.klemela at oulu.fi
Phone: +358-8-553 1754
Fax: +358-8-553 1730
Office: Pentti Kaiteran katu 1, M219

Research interests

Nonparametric function estimation

Jussi Klemelä

Teaching

Vanhoja kursseja

Book

The homepage of the book contains preview, blog, software, the figures, and advice to their reproduction.

Preprints

Preprints

Software

  • R-package "denpro" for the visualization of multivariate density estimates.

  • R-package "delt" for the estimation of multivariate densities with adaptive histograms.

  • R-package "finatool" for portfolio selection and pricing and hedging of options. (Under development)

Selected publications

  • J. Klemelä and E. Mammen. (2010). Empirical risk minimization in inverse problems Ann. Statist. 38(1): 482-511.

  • J. Klemelä. (2007). Visualization of multivariate data with tail trees. Information Visualization 6: 109-122.

  • J. Klemelä. (2006). Sharp adaptive estimation of quadratic functionals. Probab. Theory Relat. Fields. 134(4): 539-564.

  • J. Klemelä. (2004). Visualization of multivariate density estimates with level set trees. J. Comput. Graph. Statist. 13(3): 599-620.

  • J. Klemelä and A. B. Tsybakov. (2001). Sharp adaptive estimation of linear functionals. Ann. Statist. 29: 1567-1600.
Other publications

Animations

Talks

  • Analysis of dependency with density estimation (PDF)
  • Level set trees and the analysis of shapes (PDF)
  • Likelihood subsetting of financial data (PDF)
  • Empirical risk minimization in inverse problems (PDF)
  • Density estimation with stagewise optimization of the empirical risk (PDF)
  • Visualization of multivariate functions, sets, and data (PDF)
  • Visualization of multivariate density estimates with shape trees (PDF)
  • Visualization of multivariate density estimates (PDF)

Lectures on nonparametric function estimation

In nonparametric function estimation one approximates and interpolates functions when only noisy data is available. Functions to be estimated include probability density functions, regression functions, spectral density functions, and intensity functions. The lectures will concentrate on the estimation and visualization of multivariate density functions, but the techniques apply also to other kinds of functions. The estimation techniques include the use of anisotropic kernel estimators, minimization estimators, multivariate adaptive histograms, wavelet estimators, best basis selection, and stagewise minimization.

  • Lecture I, 1.10.2007 (PDF)
  • Lecture II, 15.10.2007 (PDF)
  • Lecture III, 29.10.2007 (PDF)
  • Lecture IV, 12.11.2007 (PDF)
  • Lecture V, 26.11.2007 (PDF)
  • Lecture VI, 10.12.2007 (PDF)