851_lectures17_24

# Moreover xn yn is a sequence of non negative simple

This preview shows page 1. Sign up to view the full content.

This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: s Inequality) If X ∈ L2 , then E(X 2 ) P {|X | ≥ a} ≤ a2 for every a > 0. 19–3 Statistics 851 (Fall 2013) Prof. Michael Kozdron October 23, 2013 Lecture #20: Proofs of the Main Expectation Theorems Our goal for today is to start proving all of the important results for expectation that were stated last lecture. Note that the proofs generally follow the so-called standard machine; that is, we ﬁrst prove the result for simple random variables, then extend it to non-negative random variables, and ﬁnally extend it to general random variables. The key results for implementing this strategy are Proposition 18.1 and Proposition 18.2. Theorem 20.1. Let (Ω, F , P) be a probability space. If L1 denotes the space of integrable random variables, namely L1 = {random variables X such that E(X ) < ∞}, then L1 is a vector space and expectation is a linear operator on L1 . Moreover, expectation is monotone in the sense that if X and Y are random variables with 0 ≤ X ≤ Y and Y ∈ L1 , then X ∈ L1 and 0 ≤ E(X ) ≤ E(Y ). Proof. Suppose that X ≥ 0 and Y ≥ 0 are non-negative random variables,...
View Full Document

## This document was uploaded on 04/07/2014.

Ask a homework question - tutors are online