TY - GEN
T1 - DDα-classification of asymmetric and fat-tailed data
AU - Lange, Tatjana
AU - Mosler, Karl
AU - Mozharovskyi, Pavlo
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2014.
PY - 2014/1/1
Y1 - 2014/1/1
N2 - The DDα-procedure is a fast nonparametric method for supervised classification of d-dimensional objects into q ≥ 2 classes. It is based on q-dimensional depth plots and the α-procedure, which is an efficient algorithm for discrimination in the depth space [0, 1]q. Specifically, we use two depth functions that are well computable in high dimensions, the zonoid depth and the random Tukey depth, and compare their performance for different simulated data sets, in particular asymmetric elliptically and t-distributed data.
AB - The DDα-procedure is a fast nonparametric method for supervised classification of d-dimensional objects into q ≥ 2 classes. It is based on q-dimensional depth plots and the α-procedure, which is an efficient algorithm for discrimination in the depth space [0, 1]q. Specifically, we use two depth functions that are well computable in high dimensions, the zonoid depth and the random Tukey depth, and compare their performance for different simulated data sets, in particular asymmetric elliptically and t-distributed data.
UR - https://www.scopus.com/pages/publications/84951853065
U2 - 10.1007/978-3-319-01595-8_8
DO - 10.1007/978-3-319-01595-8_8
M3 - Conference contribution
AN - SCOPUS:84951853065
SN - 9783319015941
T3 - Studies in Classification, Data Analysis, and Knowledge Organization
SP - 71
EP - 78
BT - Data Analysis, Machine Learning and Knowledge Discovery
A2 - Schmidt-Thieme, Lars
A2 - Janning, Ruth
A2 - Spiliopoulou, Myra
PB - Kluwer Academic Publishers
T2 - 36th Annual Conference of the German Classification Society on Data Analysis, Machine Learning and Knowledge Discovery, GfKl 2012
Y2 - 1 August 2012 through 3 August 2012
ER -