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Math Help - Cumulative distribution function to obtain moments?

  1. #1
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    Cumulative distribution function to obtain moments?

    I am given a continuous random variable F which is the sum of 3 exponential distributed random variables X, Y, and Z. The parameter of X is λ and the parameter for Y and Z is μ.

    X, Y and X, Z are independent, and the correlation between Y and Z is B. I am told to obtain the first 2 moments of F. How would I go about solving this problem?

    I know to find the moments for a random variable is E[x^n]=n!/
    λ^n.


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  2. #2
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    Re: Cumulative distribution function to obtain moments?

    Hey Reefer.

    Hint: For this problem you will need to consider the co-variance term and how it relates to correlation.

    Covariance and correlation - Wikipedia, the free encyclopedia

    Once you link correlation to covariance use the fact that Var[X+Y] = Var[X] + Var[Y] + 2*Cov(X,Y)
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  3. #3
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    Re: Cumulative distribution function to obtain moments?

    Isn't correlation just the covariance divided by the standard deviation of both variables? How does this help me find the first 2 moments?
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  4. #4
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    Re: Cumulative distribution function to obtain moments?

    The first moment is the mean and the second moment is E[X^2] which is Var[X] - E[X]^2, and for the correlated variable, this involves co-variance when you consider E[X+Y+Z] and E[(X+Y+Z)^2] (which are the first two standard moments).

    Also you may want to point out if they are central moments with respect to the mean or not.
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  5. #5
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    Re: Cumulative distribution function to obtain moments?

    I'm not sure if I understand it fully.. but since Y and Z are of the same parameter, can't we say that Y+Z= 2Y?

    Then from there we can find the moments to be E[X+2Y] and E[(X+2Y)^2]??
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  6. #6
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    Re: Cumulative distribution function to obtain moments?

    Y equals Z only when they have a perfect correlation (i.e. correlation = 1) which is a very special case and not a general one.
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