Tell me more ×
Cross Validated is a question and answer site for statisticians, data analysts, data miners and data visualization experts. It's 100% free, no registration required.

I'm tasked with finding the MGF of a $\chi^2$ random variable.

I think the way to do is is by using the fact that $\Sigma_{j=1}^{m} Z^2_j$ is a $\chi^2$ R.V. and that MGF of a sum is the product of the MGFs of the individual terms. Although that may not be right and it may be $E(e^{tX})$ way.

Any help would be appreciated. I don't need it solved really just need to get down the track a little further.

Thanks

share|improve this question
What's wrong with the formula given by en.wikipedia.org/wiki/Chi_squared ? i.e. $(1-2t)^{-m/2}$ – shabbychef Feb 16 '11 at 4:15
There's nothing wrong with it, but I'd like to derive it. – tshauck Feb 16 '11 at 4:30
ah, gotcha. the question does not say 'derive', however, but 'find'. – shabbychef Feb 16 '11 at 5:37
1  
sure, but contextually it's obvious I'm not looking for a link – tshauck Feb 16 '11 at 14:37

1 Answer

up vote 2 down vote accepted

Yes, since $\chi^2$ is a sum of $Z_i^2$ the MGF is a product of individual summands. But then you need the MGF of $Z_i^2$ which is $\chi^2$ with 1 degree of freedom. The obvious way of calculating the MGF of $\chi^2$ is by integrating. It is not that hard:

$$Ee^{tX}=\frac{1}{2^{k/2}\Gamma(k/2)}\int_0^\infty x^{k/2-1}e^{-x(1/2-t)}dx$$

Now do the change of variables $y=x(1/2-t)$, then note that you get Gamma function and the result is yours. If you want deeper insights (if there are any) try asking at http://math.stackexchange.com.

share|improve this answer
Thanks for the help. – tshauck Feb 16 '11 at 18:28

Your Answer

 
discard

By posting your answer, you agree to the privacy policy and terms of service.

Not the answer you're looking for? Browse other questions tagged or ask your own question.