851_lectures17_24

# Since x2 ex 2 2xexy ey 2 is a non negative quadratic

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Unformatted text preview: ) = E( Y 1A + Y 1Ac ) = E( Y 1A ) + E( Y 1Ac ) = E( Y 1A ) + E( X 1Ac ) . ( ∗) We know that there exist sequences Xn and Yn of non-negative simple random variables such that (i) Xn ↑ X and E(Xn ) ↑ E(X ), and (ii) Yn ↑ Y and E(Yn ) ↑ E(Y ). Thus, Xn 1A ↑ X 1A and E(Xn 1A ) ↑ E(X 1A ) and similarly Yn 1A ↑ Y 1A and E(Yn 1A ) ↑ E(Y 1A ). For each n, the random variable Xn takes on ﬁnitely many values and is therefore bounded by K , say, where K may depend on n. Thus, since Xn ≤ K , we obtain Xn 1A ≤ K and so 0 ≤ E(Xn 1A ) ≤ E(K 1A ) = K P {A} = 0. This implies that E(Xn 1A ) = 0 and so by uniqueness of limits, E(X 1A ) = 0. Similarly, E(Y 1A ) = 0. Therefore, by (∗), we obtain E( Y ) = E( Y 1A ) + E( X 1Ac ) = 0 + E( X 1Ac ) = E( X 1A ) + E( X 1Ac ) = E( X ) . In general, note that X = Y almost surely implies that X + = Y + almost surely and X − = Y − almost surely. 20–2 Statistics 851 (Fall 2013) Prof. Michael Kozdron October 25, 2013 Lecture #21: Proofs of the Main Expectation Theorems (continued) We will continue pro...
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## This document was uploaded on 04/07/2014.

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