- 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 regression",
-
URL = "
https://eldorado.tu-dortmund.de/bitstream/2003/26047/4/NunkesserDissertation.pdf",
-
URL = "
http://hdl.handle.net/2003/26047",
- oai = "oai:dspace.hrz.tu-dortmund.de:2003/26047",
- 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 = "In english.
NunkesserDissertation.pdf slides http://ls2-www.cs.uni-dortmund.de/%7Enunkesser/theses/phd/Algorithms_for_Regression_and_Classification.pdf deutsch (url broken Jan 2023)",
