Help with chi square distribution

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To demonstrate that the sum of two chi-square distributions, X1 with n degrees of freedom and X2 with m degrees of freedom, results in a chi-square distribution with n+m degrees of freedom, moment generating functions (MGFs) can be utilized. The MGF for X1 is given by MX1=(1-2t)^(-n/2) and for X2 as MX2=(1-2t)^(-m/2). The combined MGF, MX1+X2, is calculated as the product of the individual MGFs, yielding MX1+X2=(1-2t)^(-(n+m)/2), which corresponds to the MGF of a chi-square distribution with (n+m) degrees of freedom. Additionally, deriving the characteristic function from the MGF can further support this conclusion. This approach effectively shows the additive property of chi-square distributions.
sneaky666
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How do i show that the a [X1 has a chi square distribution with n degrees of freedom] + [X2 has a chi square distribution with m degrees of freedom] is a [X1+X2 has a chi square distribution with n+m degrees of freedom]?

How can i use moment generating functions to do this?
 
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sneaky666 said:
How do i show that the a [X1 has a chi square distribution with n degrees of freedom] + [X2 has a chi square distribution with m degrees of freedom] is a [X1+X2 has a chi square distribution with n+m degrees of freedom]?

How can i use moment generating functions to do this?

MX1=(1-2t)-n/2
MX2=(1-2t)-m/2

MX1+X2=MX1*MX1= (1-2t)-(n+m)/2

which is the MGF of a chi-square distribution with (n+m) degrees of freedom.

That's all I can come up with...not terribly good with this...
 
sneaky666 said:
How do i show that the a [X1 has a chi square distribution with n degrees of freedom] + [X2 has a chi square distribution with m degrees of freedom] is a [X1+X2 has a chi square distribution with n+m degrees of freedom]?

How can i use moment generating functions to do this?

If you want to show that a given set of random variables has a chi square distribution and that these distributions are additive you need to start with the Gaussian and then derive the characteristic function from the MGF.

http://www.planetmathematics.com/CentralChiDistr.pdf
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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