Algorithms for regression and classification - Robust regression and genetic association studies

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Robin Nunkesser",
  title =        "Algorithms for regression and classification - Robust
                 regression and genetic association studies",
  school =       "Dortmund University of Technology",
  year =         "2009",
  address =      "Germany",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, SNP, GPAS,
                 association studies, Computational statistics, Robust
  URL =          "",
  URL =          "",
  oai =          "",
  language =     "en",
  issue_date =   "2009-03-12T10:57:58Z",
  size =         "129 pages",
  abstract =     "Regression and classification are statistical
                 techniques that may be used to extract rules and
                 patterns out of data sets. Analyzing the involved
                 algorithms comprises interdisciplinary research that
                 offers interesting problems for statisticians and
                 computer scientists alike. The focus of this thesis is
                 on robust regression and classification in genetic
                 association studies. In the context of robust
                 regression, new exact algorithms and results for robust
                 online scale estimation with the estimators Qn and Sn
                 and for robust linear regression in the plane with the
                 estimator least quartile difference (LQD) are
                 presented. Additionally, an evolutionary computation
                 algorithm for robust regression with different
                 estimators in higher dimensions is devised. These
                 estimators include the widely used least median of
                 squares (LMS) and least trimmed squares (LTS).

                 For classification in genetic association studies, this
                 thesis describes a Genetic Programming algorithm that
                 outperforms the standard approaches on the considered
                 data sets. It is able to identify interesting genetic
                 factors not found before in a data set on sporadic
                 breast cancer and to handle larger data sets than the
                 compared methods. In addition, it is extendible to
                 further application fields.",
  notes =        "NunkesserDissertation.pdf slides

Genetic Programming entries for Robin Nunkesser