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@inproceedings{1088322,
author = {Katina, Stanislav},
address = {Leeds},
booktitle = {Systems Biology & Statistical Bioinformatics},
edition = {1},
editor = {Mardia K.V.},
keywords = {simplicial depth; shape analysis},
howpublished = {elektronická verze "online"},
language = {eng},
location = {Leeds},
isbn = {978-0-85316-263-6},
pages = {51-54},
publisher = {The University of Leeds},
title = {Shape analysis in the light of simplicial depth estimators},
url = {http://www1.maths.leeds.ac.uk/statistics/workshop/lasr2007/proceedings/},
year = {2010}
}
TY - JOUR
ID - 1088322
AU - Katina, Stanislav
PY - 2010
TI - Shape analysis in the light of simplicial depth estimators
PB - The University of Leeds
CY - Leeds
SN - 9780853162636
KW - simplicial depth
KW - shape analysis
UR - http://www1.maths.leeds.ac.uk/statistics/workshop/lasr2007/proceedings/
L2 - http://www1.maths.leeds.ac.uk/statistics/workshop/lasr2007/proceedings/
N2 - In this paper we present the maximum simplicial depth estimator and compare it to the ordinary least square estimator in examples from 2D shape analysis focusing on bivariate and multivariate allometrical problems from zoology. We compare two types of estimators derived under different subsets of parametric space on the basis of the linear regression model. In applications where outliers in the x- or y-axis direction occur in the data and residuals from ordinary least-square (OLS) linear regression model are not normally distributed, we recommend the use of the maximum simplicial depth estimators.
ER -
KATINA, Stanislav. Shape analysis in the light of simplicial depth estimators. Online. In Mardia K.V. \textit{Systems Biology \&{} Statistical Bioinformatics}. 1. vyd. Leeds: The University of Leeds, 2010, s.~51-54. ISBN~978-0-85316-263-6.
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