# Expectation: Useful properties and inequalities

If ${X \geq 0}$ is a random variable on ${(\Omega, \mathcal{F}, P)}$. The expected value of ${X }$ is defined as

$\displaystyle \mathbb{E}(X) \equiv \int_{\Omega} X dP = \int_{\Omega} X(\omega) P (d \omega)$

Inequalities

• Jensen’s inequality. If ${\varphi}$ is convex and ${E|X|, E|\varphi(X)| < \infty}$

$\displaystyle \mathbb{E} (\varphi(X)) \geq \varphi(\mathbb{E}X)$

• Holder’s inequality. If ${p,q \in [1, \infty]}$ with ${1/p + 1/q =1}$ then

$\displaystyle \mathbb{E}|XY| \leq \|X\|_p \|Y\|_q$

• Cauchy-Schwarz Inequality: For ${p=q=2}$

$\displaystyle \mathbb{E}|XY| \leq \left( \mathbb{E}(X^2) \mathbb{E}(Y^2) \right)^{1/2}$

Let ${Y \geq 0}$ with ${\mathbb{E} (Y^2) < \infty}$. $\displaystyle \mathbb{E}(Y)^2 \leq \mathbb{E}(Y \mathbb{I}_{Y>0})^2 \leq \mathbb{E}(Y^2) P(Y>0)$
hence

$\displaystyle P(Y>0) \geq E(Y)^2/E(Y^2)$

• Markov’s/Chebyshev’s inequality. Suppose ${\varphi: \mathbb{R} \rightarrow \mathbb{R}}$ has ${\varphi \geq 0}$, let ${\mathcal{A} \in \mathcal{R}}$ and let ${i_A = \inf \{\varphi(y) : y \in A\}}$

$\displaystyle i_A P(X \in A) \leq \mathbb{E}(\varphi(X); X \in A) \leq \mathbb{E}(\varphi(X))$

where

$\displaystyle \mathbb{E}(X;A) = \int_A X dP, \qquad A \subset \Omega$

Alternatively suppose ${X \in m \mathcal{F}}$ and that ${\varphi: \mathbb{R} \rightarrow [0,\infty]}$ is ${\mathcal{B}}$-measurable and non-decreasing (note ${\varphi (X) = \varphi \circ X \in (m \mathcal{F})^+}$ ). Then

$\displaystyle \varphi(a)P(X \geq A) \leq \mathbb{E}(\varphi(X); X \geq a) \leq \mathbb{E}(\varphi(X))$

Example:

$\displaystyle X \in \mathcal{L}^1, \qquad aP(|X| \geq a) \leq \mathbb{E}(|X|) \qquad (a>0)$

Integration to the limit

${X_n}$ is uniformly integrable if ${ \forall \epsilon>0, \exists \delta>0}$ s.th.

$\displaystyle \sup_{n \geq 1} \int_A |X_n| dP < \epsilon$

whenever ${P(A)< \delta }$ and

$\displaystyle \sup_{n \geq 1} \mathbb{E} |X_n| < \infty.$

Monotone Convergence Theorem: If ${0 \leq X_n \uparrow X}$ then ${\mathbb{E}(X_n) \uparrow \mathbb{E}(X)}$.

Lemma: A measurable function ${X}$ is integrable iff ${\forall \epsilon>0 \exists \delta>0 \text{ s.th. } A \in \mathcal{F}, P(A)< \delta}$ implies

$\displaystyle \int_A |X| dP< \epsilon, \qquad \mathbb{E}|X| \leq \frac{1}{\delta} .$

Fatou’s lemma. If ${X_n \geq 0}$ then

$\displaystyle \liminf_{n \rightarrow \infty} \mathbb{E}(X_n) \geq \mathbb{E}(\liminf_{n \rightarrow \infty}X_n)$

Dominated Convergence Theorem. If ${X_n \xrightarrow{a.s.} X}$, ${|X_n| \leq Y \text{ } \forall n}$, and ${\mathbb{E}(Y) < \infty}$, then

${\mathbb{E}(X_n) \rightarrow \mathbb{E}(X).}$

Note when ${Y}$ is constant ${\Longrightarrow}$ Bounded Convergence Theorem.

Theorem: Suppose ${X_n \xrightarrow{a.s.} X}$ and ${g,h}$ continous functions with i) ${g \geq 0}$ and ${g(x) > 0}$ when ${|x|}$ large. ii) ${|h(x)|/g(x) \rightarrow 0}$ as ${|x| \rightarrow \infty}$, and iii) ${\mathbb{E}(g(X_n)) \leq K < \infty}$ ${\forall n}$. Then

${\mathbb{E}(h(X_n)) \rightarrow \mathbb{E}(h(X))}$.

Sums of non-negative Random Variables: Collection of useful results

• If ${ X \in (m \mathcal{F})^+}$ and ${ \mathbb{E}(X) < \infty}$ then

${P(X< \infty)=1}$.

• If ${\{Z_n\}_n \in (m \mathcal{F})^+}$, then (see Monotone Convergence Theorem)

$\displaystyle \mathbb{E}(\sum Z_n) = \sum \mathbb{E}(Z_k) \leq \infty.$

• If ${\{Z_n\}_n \in (m \mathcal{F})^+}$ s.th. ${\mathbb{E}(\sum Z_n) \leq \infty}$, then

$\displaystyle \sum Z_n < \infty \text{ a.s. and so } Z_n \xrightarrow{a.s.}0$

• First Borel-Cantelli Lemma. Suppose ${\{A_k\} \in \mathcal{F}}$ sequence of events s.th. ${ \sum P(A_k) < \infty}$. Set ${Z_k = \mathbb{I}_{A_k}}$. Then

${\mathbb{E}(Z_k) = P(Z_k)}$

and by previous result

$\displaystyle \sum Z_k = \sum \mathbb{I}_{A_k} < \infty$

Note ${ \sum \mathbb{I}_{A_k} = P(\limsup_k A_k) = \cap_{n=1}^{\infty} \cup_{k=n}^{\infty} A_k }$.

Computing Expected Values

Change of variables formula. Let ${X: (\Omega, \mathcal{F}) \rightarrow (S, \mathcal{S})}$ with measure ${\mu}$, i.e. ${\mu (A) = P(X \in A)}$. If ${f: (S, \mathcal{S}) \rightarrow (\mathbb{R}, \mathcal{R})}$, so that ${f \geq 0}$ or ${ \mathbb{E}|f(x)| < \infty}$, then

$\displaystyle \mathbb{E} (f(x)) = \int_S f(y) \mu (dy)$