851_lectures17_24

# A2 computing expectations having proved a number of

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Unformatted text preview: ving the important results for expectation that were stated in Lecture #19. Recall that a random variable is said to have ﬁnite mean or have ﬁnite expectation or be integrable if E(X ) &lt; ∞. The vector space of all integrable random variable on a given probability space (Ω, F , P ) is denoted by L1 and if 1 ≤ p &lt; ∞, then Lp = {random variables X : Ω → R such that |X |p ∈ L1 }. Theorem 21.1 (Cauchy-Schwartz Inequality). If X , Y ∈ L2 , then XY ∈ L1 and ￿ |E(XY )| ≤ E(X 2 )E(Y 2 ). Proof. Since 0 ≤ (X + Y )2 = X 2 + Y 2 + 2XY and 0 ≤ (X − Y )2 = X 2 + Y 2 − 2XY , we conclude that 2|XY | ≤ X 2 + Y 2 implying 2E(|XY |) ≤ E(X 2 ) + E(Y 2 ). Thus, if X, Y ∈ L2 , we conclude that XY ∈ L1 . For every x ∈ R, note that 0 ≤ E((xX + Y )2 ) = x2 E(X 2 ) + 2xE(XY ) + E(Y 2 ). Since x2 E(X 2 ) + 2xE(XY ) + E(Y 2 ) is a non-negative quadratic in x, its discriminant is necessarily non-positive; that is, 4[E(XY )]2 − 4E(X 2 )E(Y 2 ) ≤ 0, or, equivalently, |E(XY )| ≤ E(X 2 )E(Y 2 ) as required. Theorem 21.2. Let X : (Ω, F , P) → (R, B ) be a random variable. (a) (Markov’s Inequality) If X ∈ L1 , then P {|X | ≥ a} ≤ E(|X |) a for every a &gt; 0. (b) (Chebychev’s...
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