Challenges in Statistical Theory: Complex Data Structures and Algorithmic Optimization
Claudia KlüppelbergTU München, Garching, Germany
Rudolf J. BeranUniversity of California at Davis, USA
Wolfgang PolonikUniversity of California at Davis, USA
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. 2179–2234DOI 10.4171/OWR/2009/39