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Change of random variables

  1. Jul 17, 2011 #1
    Hoel: An Introduction to Mathematical Statistics introduces the following formulas for expectation, where the density is zero outside of the interval [a,b].

    [tex]E\left [ X \right ] = \int_{a}^{b} x f(x) \; dx[/tex]

    [tex]E\left [ g(X) \right ] = \int_{a}^{b} g(x) f(x) \; dx[/tex]

    He says, "Let the random variable g(X) be denoted by Y. Then knowing the density f(x) of X it is theoretically possible to to find the density h(x) of Y. The expected value of g(X) is the same as the expected value of Y; therefore if h(y) is available, the latter expected value can be expressed in the form

    [tex]E\left [ Y \right ] = \int_{-\infty}^{\infty} y h(y) \; dy.[/tex]

    "By using the change of variable techniques of calculus, it can be shown that this value is the same as the value given by (22) [the 2nd formula I've quoted in this post]."

    I've been trying to do this. Let I denote the identity function on [itex]\mathbb{R}[/itex]. Let [itex]f_X[/itex] denote the pdf of the distribution induced by a random variable X, and [itex]F_X[/itex] its cdf. I'm guessing that when the expected value of a distribution is expressed like this in terms of a random variable, [itex]E[X][/itex] is to be understood as [itex]E[P_X][/itex], and [itex]E[g(X)][/itex] as [itex]E[P_{g \circ X}][/itex], where [itex]P_X[/itex] means the distribution induced by the random variable X, given some sample space implicit in the context.

    Then expectation is defined by

    [tex]E[P_X]=\int_a^b I \cdot f_X,[/tex]

    and we must show that

    [tex]\int_a^b I \cdot f_{g \circ X} = \int_a^b g \cdot f_X,[/tex]

    or do the limits need to be changed? Using the chain rule (integration by substitution) and the identity

    [tex]F_{g \circ X}=F_X \circ g,[/tex]

    leads me to

    [tex]\int_a^b I \cdot f_{g \circ X} = \int_{g(a)}^{g(b)} I \cdot f_X[/tex]

    which looks tantalisingly close, but am I going in the right direction?
  2. jcsd
  3. Jul 17, 2011 #2
    This is sometimes called The Law of the Unconscious Statistician, so you might try looking for sources. I'm not sure how to use your approach, so I'll give a slightly different one. In Sheldon Ross's A First Course in Probability, he shows this by first proving the lemma
    \mathbf{E}[Y] =\int_0^\infty \mathbf{P}\{Y > y \} \, dy - \int_0^\infty \mathbf{P}\{Y < -y \} \, dy
    for any random variable Y. (This is a pretty straightforward proof: just switch the order of integration using the pdf for Y.) After that, he sets Y = g(X) and switching the order of integration once more, the result falls out. I can go into more detail if you'd like, but I hope this helps!
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