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<record version="1" id="3711">
 <title>Cram\'er-Wold theorem</title>
 <name>CramerWoldTheorem</name>
 <created>2002-12-10 08:49:40</created>
 <modified>2002-12-10 08:49:40</modified>
 <type>Theorem</type>
 <creator id="127" name="Koro"/>
 <author id="127" name="Koro"/>
 <classification>
	<category scheme="msc" code="60E05"/>
 </classification>
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 <content>Let 
\[\overline{X}_n = (X_{n1},\dots,X_{nk}) \;\mbox{and} \; 
\overline{X} = (X_1,\dots,X_k)\]
be \PMlinkescapetext{k-dimensional} random vectors. Then $\overline{X}_n$ converges to $\overline{X}$ \PMlinkname{in distribution}{ConvergenceInDistribution} if and only if
\[\sum_{i=1}^k t_iX_{ni} \xrightarrow[n\rightarrow\infty]{D} 
\sum_{i=1}^k t_iX_i.\]
for each $(t_1,\dots,t_k)\in \mathbb{R}^k$. That is, if every 
\PMlinkescapetext{fixed} linear combination of the coordinates of $\overline{X}_n$ converges in distribution to the correspondent linear combination of coordinates of $\overline{X}$.</content>
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