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                                              2.8 Suggestions for Further Reading             67

                        2.21 Let (X, Y) denote a two-dimensional random vector with an absolutely continuous distribution
                            with density function
                                                       1
                                               p(x, y) =  exp(−y), 0 < x < y < ∞.
                                                       y
                                   r
                            Find E(X |Y) for r = 1, 2,....
                        2.22 For some n = 1, 2,..., let Y 1 , Y 2 ,..., Y n+1 denote independent, identically distributed, real-
                            valued random variables. Define

                                                   X j = Y j Y j+1 ,  j = 1,..., n.

                            (a) Are X 1 , X 2 ,..., X n independent?
                            (b) Are X 1 , X 2 ,..., X n exchangeable?
                        2.23 Let X and Y denote real-valued exchangeable random variables. Find Pr(X ≤ Y).
                        2.24 Prove Theorem 2.7.
                        2.25 Let X 0 , X 1 ,..., X n denote independent, identically distributed real-valued random variables.
                            For each definition of Y 1 ,..., Y n given below, state whether or not Y 1 ,..., Y n are exchangeable
                            and justify your answer.
                            (a) Y j = X j − X 0 , j = 1,..., n.
                            (b) Y j = X j − X j−1 , j = 1,..., n.
                                                              n
                            (c) Y j = X j − ¯ X, j = 1,..., n where ¯ X =  j=1  X j /n.
                            (d) Y j = ( j/n)X j + (1 − j/n)X 0 , j = 1,..., n.
                        2.26 Let Y 1 , Y 2 ,... denote independent, identically distributed nonnegative random variables with
                            E(Y 1 ) = 1. For each n = 1, 2,..., let

                                                        X n = Y 1 ··· Y n .
                            Is {X 1 , X 2 ,...} a martingale?
                        2.27 Let {X 1 , X 2 ,...} denote a martingale. Show that

                                                      E(X 1 ) = E(X 2 ) = ··· .
                              Exercises 2.28 and 2.29 use the following definition. A sequence of real-valued random
                            variables {X 1 , X 2 ,...} such that E[|X n |] < ∞, n = 1, 2,..., is said to be a submartingale if,
                            for each n = 1, 2,...,

                                                    E[X n+1 |X 1 ,..., X n ] ≥ X n
                            with probability 1.
                        2.28 Show that if {X 1 , X 2 ,...} is a submartingale, then {X 1 , X 2 ,...} is a martingale if and only if

                                                      E(X 1 ) = E(X 2 ) = ··· .
                        2.29 Let {X 1 , X 2 ,...} denote a martingale. Show that {|X 1 |, |X 2 |,...} is a submartingale.



                                           2.8 Suggestions for Further Reading

                        Conditional distributions and expectation are standard topics in probability theory. A mathematically
                        rigorous treatment of these topics requires measure-theoretic probability theory; see, for example,
                        Billingsley (1995, Chapter 6) and Port (1994, Chapter 14). For readers without the necessary back-
                        ground for these references, Parzen (1962, Chapter 2), Ross (1985, Chapter 3), Snell (1988, Chapter 4),
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