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A [[Statistical test|statistical test]] for testing the hypothesis <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s091/s091800/s0918001.png" /> that a one-dimensional probability density is symmetric about zero.
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A [[Statistical test|statistical test]] for testing the hypothesis $H_0$ that a one-dimensional probability density is symmetric about zero.
  
Let the hypothesis of symmetry <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s091/s091800/s0918002.png" /> be that the probability density <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s091/s091800/s0918003.png" /> of the probability law of independent random variables <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s091/s091800/s0918004.png" /> is symmetric about zero, that is, <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s091/s091800/s0918005.png" /> for any <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s091/s091800/s0918006.png" /> from the domain of definition of <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s091/s091800/s0918007.png" />. Any statistical test intended for testing <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s091/s091800/s0918008.png" /> is called a symmetry test.
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Let the hypothesis of symmetry $H_0$ be that the probability density $p(x)$ of the probability law of independent random variables $X_1,\ldots,X_n$ is symmetric about zero, that is, $p(x)=p(-x)$ for any $x$ from the domain of definition of $p(x)$. Any statistical test intended for testing $H_0$ is called a symmetry test.
  
Most often the hypothesis <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s091/s091800/s0918009.png" /> that all the random variables <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s091/s091800/s09180010.png" /> have probability density <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s091/s091800/s09180011.png" />, <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s091/s091800/s09180012.png" />, is considered as the alternative to <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s091/s091800/s09180013.png" />. In other words, according to <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s091/s091800/s09180014.png" /> the probability density of <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s091/s091800/s09180015.png" /> is obtained by shifting the density <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s091/s091800/s09180016.png" /> along the <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s091/s091800/s09180017.png" />-axis by a distance <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s091/s091800/s09180018.png" />, to the right or left according to the sign of <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s091/s091800/s09180019.png" />. If the sign of the displacement <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s091/s091800/s09180020.png" /> is known, then <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s091/s091800/s09180021.png" /> is called one-sided, otherwise it is called two-sided. A simple example of a symmetry test is given by the [[Sign test|sign test]].
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Most often the hypothesis $H_1$ that all the random variables $X_1,\ldots,X_n$ have probability density $p(x-\Delta)$, $\Delta\neq0$, is considered as the alternative to $H_0$. In other words, according to $H_1$ the probability density of $X_i$ is obtained by shifting the density $p(x)$ along the $x$-axis by a distance $|\Delta|$, to the right or left according to the sign of $\Delta$. If the sign of the displacement $\Delta$ is known, then $H_1$ is called one-sided, otherwise it is called two-sided. A simple example of a symmetry test is given by the [[Sign test|sign test]].
  
 
Usually a [[Randomization test|randomization test]] is used for testing symmetry.
 
Usually a [[Randomization test|randomization test]] is used for testing symmetry.

Latest revision as of 16:19, 19 April 2014

A statistical test for testing the hypothesis $H_0$ that a one-dimensional probability density is symmetric about zero.

Let the hypothesis of symmetry $H_0$ be that the probability density $p(x)$ of the probability law of independent random variables $X_1,\ldots,X_n$ is symmetric about zero, that is, $p(x)=p(-x)$ for any $x$ from the domain of definition of $p(x)$. Any statistical test intended for testing $H_0$ is called a symmetry test.

Most often the hypothesis $H_1$ that all the random variables $X_1,\ldots,X_n$ have probability density $p(x-\Delta)$, $\Delta\neq0$, is considered as the alternative to $H_0$. In other words, according to $H_1$ the probability density of $X_i$ is obtained by shifting the density $p(x)$ along the $x$-axis by a distance $|\Delta|$, to the right or left according to the sign of $\Delta$. If the sign of the displacement $\Delta$ is known, then $H_1$ is called one-sided, otherwise it is called two-sided. A simple example of a symmetry test is given by the sign test.

Usually a randomization test is used for testing symmetry.

References

[1] Z. Sidak, "Theory of rank tests" , Acad. Press (1967)
[2] M.G. Kendall, A. Stuart, "The advanced theory of statistics" , 2. Inference and relationship , Griffin (1979)
How to Cite This Entry:
Symmetry test. Encyclopedia of Mathematics. URL: http://encyclopediaofmath.org/index.php?title=Symmetry_test&oldid=19088
This article was adapted from an original article by M.S. Nikulin (originator), which appeared in Encyclopedia of Mathematics - ISBN 1402006098. See original article