Essentials of Discrete Mathematics
3rd Edition
ISBN: 9781284056242
Author: David J. Hunter
Publisher: Jones & Bartlett Learning
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Theorem 2.4 (The Hölder inequality)
Let p+q=1. If E|X|P < ∞ and E|Y| < ∞, then
.
EXY SEXY ≤ Xp Yq.
1 for all k, and set o
(ii) Let X1, X2,
that P(Xkb) =
x > 0.
Xn be independent random variables with mean 0, suppose
=
and
Var Xk. Then, for
0x) ≤2 exp-tx+121
Στ
k=1
Lemma 1.1 Suppose that g is a non-negative, non-decreasing function such that
E g(X) 0. Then,
E g(|X|)
P(|X|> x) ≤
g(x)
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- Proof of this Theorem Theorem 1.2 (i) Suppose that P(|X| ≤ b) = 1 for some b > 0, that E X = 0, and set Var X = o². Then, for 0 0, P(X > x) ≤ e−1x+1²², P(|X|> x) ≤ 2e−x+1² 0²arrow_forwardState and prove the Morton's inequality Theorem 1.1 (Markov's inequality) Suppose that E|X|" 0, and let x > 0. Then, E|X|" P(|X|> x) ≤ x"arrow_forward(iii) If, in addition, X1, X2, ... Xn are identically distributed, then P(S|>x) ≤2 exp{-tx+nt²o}}.arrow_forward
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