On functional measures of skewness

Renata Dziubińska; Dominik Szynal

Applicationes Mathematicae (1996)

  • Volume: 23, Issue: 4, page 395-403
  • ISSN: 1233-7234

Abstract

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We introduce a concept of functional measures of skewness which can be used in a wider context than some classical measures of asymmetry. The Hotelling and Solomons theorem is generalized.

How to cite

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Dziubińska, Renata, and Szynal, Dominik. "On functional measures of skewness." Applicationes Mathematicae 23.4 (1996): 395-403. <http://eudml.org/doc/219141>.

@article{Dziubińska1996,
abstract = {We introduce a concept of functional measures of skewness which can be used in a wider context than some classical measures of asymmetry. The Hotelling and Solomons theorem is generalized.},
author = {Dziubińska, Renata, Szynal, Dominik},
journal = {Applicationes Mathematicae},
keywords = {mean; standard deviation; mixture of distribution functions; quantiles; conditional distributions; Pareto distributions; the Pearson coefficient of skewness; median; Pearson coefficient of skewness},
language = {eng},
number = {4},
pages = {395-403},
title = {On functional measures of skewness},
url = {http://eudml.org/doc/219141},
volume = {23},
year = {1996},
}

TY - JOUR
AU - Dziubińska, Renata
AU - Szynal, Dominik
TI - On functional measures of skewness
JO - Applicationes Mathematicae
PY - 1996
VL - 23
IS - 4
SP - 395
EP - 403
AB - We introduce a concept of functional measures of skewness which can be used in a wider context than some classical measures of asymmetry. The Hotelling and Solomons theorem is generalized.
LA - eng
KW - mean; standard deviation; mixture of distribution functions; quantiles; conditional distributions; Pareto distributions; the Pearson coefficient of skewness; median; Pearson coefficient of skewness
UR - http://eudml.org/doc/219141
ER -

References

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  1. [1] H. Hotelling and L. M. Solomons, The limits of a measure of skewness, Ann. Math. Statist. 3 (1932), 141-142. Zbl58.1165.02
  2. [2] C. A. O'Cinneide, The mean is within one standard deviation of any median, Amer. Statist. 44 (1990), 292-293. 
  3. [3] I. Olkin, A matrix formulation on how deviant an observation can be, ibid. 46 (1992), 205-209. 

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