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                            30                    Properties of Probability Distributions

                            Hence,

                                            E(Y) =          yp(x) =         g(x)p(x).
                                                   y  x:g(x)=y      y  x:g(x)=y
                            Since every x value in the range of X leads to some value y in the range of Y,it follows that


                                                       E(Y) =    g(x)p(x).
                                                               x
                              In the general case, the result is simply the change-of-variable formula for integration, as
                            discussed in Appendix 1; see, for example, Billingsley (1995, Theorem 16.13) for a proof.
                              The result (1.3) is usually expressed without reference to the random variable Y:

                                                     E[g(X)] =   g(x) dF(x)
                                                               X
                            provided that the integral exists. Note that it causes no problem if g(x)is undefined for x ∈ A
                            for some set A such that Pr(X ∈ A) = 0; this set can simply be omitted when computing
                            the expected value.

                            Example 1.31 (Standard exponential distribution). Let X denote a random variable with
                            a standard exponential distribution; see Example 1.16. Then X has density function
                                                   p(x) = exp(−x),  0 < x < ∞.

                                                                                  r
                                                      r
                            Consider the expected value of X where r > 0isa constant. If E(X )exists, it is given by
                                                         ∞

                                                            r
                                                           x exp(−x) dx,
                                                        0
                            which is simply the well-known gamma function evaluated at r + 1,  (r + 1); the gamma
                            function is discussed in detail in Section 10.2.
                                                         r
                              Expected values of the form E(X ) for r = 1, 2,... are called the moments of the dis-
                            tribution or the moments of X. Thus, the moments of the standard exponential distribution
                            are r!, r = 1, 2,.... Moments will be discussed in detail in Chapter 4.


                            Example 1.32 (Uniform distribution on the unit cube). Let X denote a three-dimensional
                                                                     3
                            random vector with the uniform distribution on (0, 1) ; see Example 1.30. Let Y = X 1 X 2 X 3 ,
                            where X = (X 1 , X 2 , X 3 ). Then
                                                                                1
                                                       1     1     1
                                              E(Y) =          x 1 x 2 x 3 dx 1 dx 2 dx 3 =  .
                                                     0   0  0                   8
                              Given the correspondence between E[g(X)] and integrals of the form

                                                            g(x) dF(x),
                                                           X
                            many important properties of expectation may be derived directly from the corresponding
                            properties of integrals, given in Appendix 1. Theorem 1.10 contains a number of these; the
                            proof follows immediately from the results in Appendix 1 and, hence, it is omitted.
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