Difference between revisions of "Student distribution"
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<table class="eq" style="width:100%;"> <tr><td valign="top" style="width:94%;text-align:center;"><img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s090/s090710/s0907105.png" /></td> </tr></table> | <table class="eq" style="width:100%;"> <tr><td valign="top" style="width:94%;text-align:center;"><img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s090/s090710/s0907105.png" /></td> </tr></table> | ||
− | where <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s090/s090710/s0907106.png" /> is a random variable subject to the standard normal law <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s090/s090710/s0907107.png" /> and <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s090/s090710/s0907108.png" /> is a random variable not depending on <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s090/s090710/s0907109.png" /> and subject to the [[ | + | where <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s090/s090710/s0907106.png" /> is a random variable subject to the standard normal law <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s090/s090710/s0907107.png" /> and <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s090/s090710/s0907108.png" /> is a random variable not depending on <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s090/s090710/s0907109.png" /> and subject to the [[Chi-squared distribution| "chi-squared" distribution]] with <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s090/s090710/s09071010.png" /> degrees of freedom. The distribution function of the random variable <img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s090/s090710/s09071011.png" /> is expressed by the formula |
<table class="eq" style="width:100%;"> <tr><td valign="top" style="width:94%;text-align:center;"><img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s090/s090710/s09071012.png" /></td> </tr></table> | <table class="eq" style="width:100%;"> <tr><td valign="top" style="width:94%;text-align:center;"><img align="absmiddle" border="0" src="https://www.encyclopediaofmath.org/legacyimages/s/s090/s090710/s09071012.png" /></td> </tr></table> |
Revision as of 11:51, 20 October 2012
with degrees of freedom, -distribution
The probability distribution of the random variable
where is a random variable subject to the standard normal law and is a random variable not depending on and subject to the "chi-squared" distribution with degrees of freedom. The distribution function of the random variable is expressed by the formula
In particular, if , then
is the distribution function of the Cauchy distribution. The probability density of the Student distribution is symmetric about 0, therefore
The moments of a Student distribution exist only for , the odd moments are equal to 0, and, in particular . The even moments of a Student distribution are expressed by the formula
in particular, . The distribution function of the random variable is expressed in terms of the beta-distribution function in the following way:
where is the incomplete beta-function, . If , then the Student distribution converges to the standard normal law, i.e.
Example. Let be independent, identically, normally -distributed random variables, where the parameters and are unknown. Then the statistics
are the best unbiased estimators of and ; here and are stochastically independent. Since the random variable is subject to the standard normal law, while
is distributed according to the "chi-squared" law with degrees of freedom, then by virtue of their independence, the fraction
is subject to the Student distribution with degrees of freedom. Let and be the solutions of the equations
Then the statistics and are the lower and upper bounds of the confidence set for the unknown mathematical expectation of the normal law , and the confidence coefficient of this confidence set is equal to , i.e.
The Student distribution was first used by W.S. Gosset (pseudonym Student).
References
[1] | H. Cramér, "Mathematical methods of statistics" , Princeton Univ. Press (1946) |
[2] | L.N. Bol'shev, N.V. Smirnov, "Tables of mathematical statistics" , Libr. math. tables , 46 , Nauka (1983) (In Russian) (Processed by L.S. Bark and E.S. Kedrova) |
[3] | "Student" (W.S. Gosset), "The probable error of a mean" Biometrika , 6 (1908) pp. 1–25 |
Student distribution. Encyclopedia of Mathematics. URL: http://encyclopediaofmath.org/index.php?title=Student_distribution&oldid=28550