JournalsowrVol. 6, No. 3pp. 2179–2234

Challenges in Statistical Theory: Complex Data Structures and Algorithmic Optimization

  • Claudia Klüppelberg

    TU München, Garching, Germany
  • Rudolf J. Beran

    University of California at Davis, USA
  • Wolfgang Polonik

    University of California at Davis, USA
Challenges in Statistical Theory: Complex Data Structures and Algorithmic Optimization cover
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Abstract

Technological developments have created a constant incoming stream of complex new data structures that need analysis. Modern statistics therefore means mathematically sophisticated new statistical theory that generates or supports innovative data-analytic methodologies for complex data structures. Inherent in many of these methodologies are challenging numerical optimization methods. The proposed workshop intends to bring together experts from mathematical statistics as well as statisticians involved in serious modern applications and computing. The primary goal of this meeting was to advance the mathematical and methodological underpinnings of modern statistics for complex data. Particular focus was given to the advancement of theory and methods under non-stationarity and complex dependence structures including (multivariate) financial time series, scientific data analysis in neurosciences and bio-physics, estimation under shape constraints, and highdimensional discrimination/classification.

Cite this article

Claudia Klüppelberg, Rudolf J. Beran, Wolfgang Polonik, Challenges in Statistical Theory: Complex Data Structures and Algorithmic Optimization. Oberwolfach Rep. 6 (2009), no. 3, pp. 2179–2234

DOI 10.4171/OWR/2009/39