What is the Mean of Exponential RVs with Chi Square Distribution?

In summary, the conversation is about deriving the mean of exponential random variables that follow a chi square distribution with degree of freedom 2n. The discussion involves using the moment-generating function and the relationship between the gamma distribution and the chi square distribution. The ultimate question is how to obtain the chi square distribution by taking the average of a gamma distribution.
  • #1
zli034
107
0
I forgot how to derive the mean of exponential random variables follow the chi square distribution with degree freedom 2n. Don't know where I got it wrong. Anyone have a clue how to do it?

Thanks
 
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  • #2
zli034 said:
I forgot how to derive the mean of exponential random variables follow the chi square distribution with degree freedom 2n. Don't know where I got it wrong. Anyone have a clue how to do it?

Thanks

[tex]\mu = \int_0^{\infty}t f(t)dt[/tex] where [tex]f(t)[/tex] is the pdf.
 
  • #3
I want the distribution of the sample mean, which should has a chi square distribution. I know how to do the expectation of the sample mean, which is u.

Not quite what I am asking for. Thanks for the reply
 
  • #4
try calculating the moment-generating function of the mean (it exists, since all moments of the exponential distribution exist). Note that for ANY distribution, where all the following integrals exist, the moment generating function of the sample mean is

[tex]
m_{\overline x} (s) = E[e^{s \frac 1 n \sum x}] = E[\prod e^{(\frac s n)x}] = \prod{E[e^{(\frac s n)x}]}
[/tex]

and each factor in the final product is calculated based on the exponential distribution.
 
  • #5
I don't get it by doing MGF. Sum of exponential RVs is a Gamma RV. Chi square is the special case of Gamma with parameter is 1/2. I don't know how to from a Gamma by taking average to get the Chi.
 
  • #6
zli034 said:
I don't get it by doing MGF. Sum of exponential RVs is a Gamma RV. Chi square is the special case of Gamma with parameter is 1/2. I don't know how to from a Gamma by taking average to get the Chi.

Well the exponential distribution is a special case of the gamma dist. where k=1. In addition, when lambda=1/2, I believe the distribution of the sample means asymptotically approaches a chi square dist. with a mean of 2 (2 degrees of freedom). If so, what exactly is your question?
 
  • #7
SW VandeCarr said:
Well the exponential distribution is a special case of the gamma dist. where k=1. In addition, when lambda=1/2, I believe the distribution of the sample means asymptotically approaches a chi square dist. with a mean of 2 (2 degrees of freedom). If so, what exactly is your question?

If you're asking about a hyper-exponential dist.; it is still a one parameter dist., not a higher form (k>1) of the gamma dist.
 

FAQ: What is the Mean of Exponential RVs with Chi Square Distribution?

What is the "mean" of exponential random variables?

The "mean" of exponential random variables is a measure of central tendency. It represents the average value of a set of exponential random variables. It is also known as the "expected value" or the "first moment" of the exponential distribution.

How is the mean of exponential random variables calculated?

The mean of exponential random variables can be calculated using the formula 1/λ, where λ (lambda) is the rate parameter of the exponential distribution. This formula is applicable for both continuous and discrete exponential random variables.

What does the mean of exponential random variables tell us?

The mean of exponential random variables provides information about the average value or location of the distribution. It can also be interpreted as the long-term average of a series of exponential events. It is useful for understanding the behavior of exponential processes and making predictions.

Can the mean of exponential random variables be negative?

No, the mean of exponential random variables cannot be negative. This is because exponential random variables always have positive values. The mean represents the center of the distribution and cannot be outside the range of possible values.

How does the mean of exponential random variables change with different rate parameters?

The mean of exponential random variables is inversely proportional to the rate parameter λ. This means that as the rate parameter increases, the mean decreases and vice versa. This relationship is important for understanding the behavior of exponential distributions and their applications in various fields.

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