LEUVEN STATISTICS RESEARCH CENTRE (LSTAT)
CELESTIJNENLAAN 200 B BOX 5307
3001 LEUVEN, BELGIË
Thesis topic (before March 13, 2014)
Title : A comparison of robust correlation estimators
OUR REFERENCE
YOUR REFERENCE
LEUVEN
Name promoter: Stefan Van Aelst
Available for students from - Biometrics, General Statistical Methodology
Description:
Indicate whether the working area is the KULeuven or a company outside KULeuven.
The motivating problem is the need to calculate many correlation measures to unravel the relation
between the major RNA parts (mRNA) and more recently explored pieces of RNA such as microRNA
(miRNA). To understand the role of these miRNA pieces, correlations are examined with many wellknown mRNA parts. Newer bio-technology tools are cheaper and faster than before, but also more
error-prone, so the resulting data may be contaminated. Given the large size of such data (hundreds to
thousands of RNA pieces), it is not feasible to ‘clean’ the data by hand. Therefore, the correlation
measures calculated from the data need to be robust. Moreover, they need to be easy to compute
because several millions of correlations need to be calculated. The purpose of this thesis is to
investigate properties of existing and new correlation measures, such as their robustness and
accuracy, but also their computation time will be examined. The comparison will involve well-known
correlation measures such as Spearman and Kendall correlation, but also correlation measures based
on robust orthogonal regression estimators. For the application the ranking of RNA pieces according to
their correlation is of great importance, so it will be investigated to what extent the rankings
corresponding to the different correlation measures differ from each other.
The thesis work will be carried out at KU Leuven.
AN CARBONEZ
TEL. + 32 16 32 22 42
An.Carbonez@lstat.kuleuven.be
lstat.kuleuven.be