Classification Methods for High-Dimensional Genetic Data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F14%3A00427225" target="_blank" >RIV/67985807:_____/14:00427225 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1016/j.bbe.2013.09.007" target="_blank" >http://dx.doi.org/10.1016/j.bbe.2013.09.007</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.bbe.2013.09.007" target="_blank" >10.1016/j.bbe.2013.09.007</a>

Result language
angličtina
Original language name
Classification Methods for High-Dimensional Genetic Data
Original language description
Standard methods of multivariate statistics fail in the analysis of high-dimensional data. This paper gives an overview of recent classification methods proposed for the analysis of high-dimensional data, especially in the context of molecular genetics.We discuss methods of both biostatistics and data mining based on various background, explain their principles, and compare their advantages and limitations. We also include dimension reduction methods tailor-made for classification analysis and also such classification methods which reduce the dimension of the computation intrinsically. A common feature of numerous classification methods is the shrinkage estimation principle, which has obtained a recent intensive attention in high-dimensional applications.
Czech name
—
Czech description
—

Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
BB - Applied statistics, operational research
OECD FORD branch
—

Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)