Reference : Sine-skewed toroidal distributions and their application in protein bioinformatics
Scientific journals : Article
Life sciences : Food science
Physical, chemical, mathematical & earth Sciences : Mathematics
http://hdl.handle.net/10993/51311
Sine-skewed toroidal distributions and their application in protein bioinformatics
English
Ley, Christophe mailto [University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Mathematics (DMATH) >]
Ameijeiras-Alonso, Jose [KU Leuven > Statistics Section, Department of Mathematics]
2-Oct-2020
Biostatistics
Oxford University Press
Yes
1465-4644
1468-4357
Oxford
United Kingdom
[en] In the bioinformatics field, there has been a growing interest in modeling dihedral angles of amino acids by viewing them as data on the torus. This has motivated, over the past years, new proposals of distributions on the torus. The main drawback of most of these models is that the related densities are (pointwise) symmetric, despite the fact that the data usually present asymmetric patterns. This motivates the need to find a new way of constructing asymmetric toroidal distributions starting from a symmetric distribution. We tackle this problem in this article by introducing the sine-skewed toroidal distributions. The general properties of the new models are derived. Based on the initial symmetric model, explicit expressions for the shape and dependence measures are obtained, a simple algorithm for generating random numbers is provided, and asymptotic results for the maximum likelihood estimators are established. An important feature of our construction is that no extra normalizing constant needs to be calculated, leading to more flexible distributions without increasing the complexity of the models. The benefit of employing these new sine-skewed toroidal distributions is shown on the basis of protein data, where, in general, the new models outperform their symmetric antecedents.
http://hdl.handle.net/10993/51311
https://doi.org/10.1093/biostatistics/kxaa039

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