Dr. Thorsten Dickhaus

Projects as a project leader

• CH-AP3

  Multiple testing under unspecified dependency structure

  Dr. Thorsten Dickhaus

  Project heads: Dr. Thorsten Dickhaus
  Project members: -
  Duration: 01.04.2012 - 31.03.2015
  Status: completed
  Located at: Humboldt Universität Berlin
  

  Description

  Multiple hypotheses testing has emerged as one of the most active research fields in statistics over the last 10-15 years, contributing at present approximately 8% of all articles in the four leading methodological statistics journals (data from Benjamini, 2010). This growing interest is especially driven by large-scale applications, such as in genomics, proteomics or cosmology. Many new multiple type I and type II error criteria like the meanwhile quite popular “false discovery rate” (FDR) have recently been propagated and published together with explicit algorithms for controlling them. A broad class of these methods employs marginal test statistics or p-values, respectively, for each individual hypothesis and a set of critical constants with which they have to be compared. Up to now, only under joint independence of all marginal p-values the behaviour of such methods is understood well. Moreover, under unspecified dependence the type I error level is often not kept accurately or not fully exhausted. This holds true especially for the FDR or related measures and offers room for improvements of those procedures with respect to type I error control and power. An adaptation to the dependency structure can therefore lead to a gain in validity (type I error rate is kept accurately) and efficiency (quantified by multiple power measures). In this project, a general theory of the usage of parametric copulae methods in this multiple testing shall be developed. This will be flanked by structural assumptions regarding the multivariate distribution of p-values reducing the complexity of the problem, for instance, the dimensionality of the copula parameter. Moreover, we will develop resampling techniques for empirical calibration of multiple testing thresholds in the case of unspecified dependency.
  
  https://www.mathematik.hu-berlin.de/de/for1735/projects_old/multipleTesting

• CH-AP4

  Statistical inference methods for behavioral genetics and neuroeconomics

  Dr. Thorsten Dickhaus

  Project heads: Dr. Thorsten Dickhaus
  Project members: -
  Duration: 01.07.2013 - 31.03.2015
  Status: completed
  Located at: Humboldt Universität Berlin
  

  Description

  The proposed project contributes to fundamental research in behavioral genetics and neuroeconomics by developing refined statistical inference methods for data generated in these fields. In particular, techniques for multiple hypotheses testing will be refined, adapted and newly worked out. Multiple tests are needed in behavioral genetics in order to analyze associations between many genetic markers and behavioral phenotypes simultaneously. In neuroeconomics, high-dimensional and spatially clustered functional magnetic resonance imaging time series have to be analyzed with multiple testing techniques. We will apply the methods resulting from the research in this project to risk preference and genetics data that we have compiled in prior work. Furthermore, our methodological contributions will be applicable in many other fields, too: High-dimensional categorical data are also prevalent, for example, in genetic epidemiology and high-dimensional hierarchical data structures occur for instance in spatial statistics or in the context of the analysis of variance with many groups.
  
  http://gepris.dfg.de/gepris/projekt/239049500

• CH-AP5

  EPILYZE - DNA Methylierungs-Signaturen als innovative Biomarker für die quantitative und qualitative Analyse von Immunzellen, Subproject C

  Dr. Thorsten Dickhaus

  Project heads: Dr. Thorsten Dickhaus
  Project members: -
  Duration: 01.12.2012 - 31.03.2015
  Status: completed
  Located at: Humboldt Universität Berlin
  

  http://foerderportal.bund.de/foekat/jsp/SucheAction.do?actionMode=view&fkz=031A191A#