Doob's Maximal Inequality/Discrete Time/Proof 2
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Theorem
Let $\struct {\Omega, \Sigma, \sequence {\FF_n}_{n \mathop \ge 0}, \Pr}$ be a filtered probability space.
Let $\sequence {X_n}_{n \mathop \ge 0}$ be a non-negative $\sequence {\FF_n}_{n \mathop \ge 0}$-submartingale.
Let:
- $\ds X_n^\ast = \max_{0 \mathop \le k \mathop \le n} X_k$
where $\max$ is the pointwise maximum.
Let $\lambda > 0$.
Then:
- $\lambda \map \Pr {X_n^\ast \ge \lambda} \le \expect {X_n}$
Proof
Let $\lambda > 0 $ and $n \ge 0$.
Let:
- $E := \set {X^\ast _n \ge \lambda}$.
That is, $E$ is a disjoint union:
- $(1):\quad \ds E = \bigsqcup _{0 \mathop \le k \mathop \le n} E_k$
where:
- $\ds E_k := \set {X_k \ge \lambda} \cap \bigcap _{0 \mathop \le j \mathop \le k-1} \set {X_j < \lambda}$
By construction, we have:
- $\forall k \in \set {0, \ldots, n} : E_k \in \FF_k$
In particular:
\(\ds \expect {X_n \chi_{E_k} \mid \FF_k }\) | \(=\) | \(\ds \expect {X_n \mid \FF_k } \chi_{E_k}\) | Rule for Extracting Random Variable from Conditional Expectation of Product | |||||||||||
\(\text {(2)}: \quad\) | \(\ds \) | \(\ge\) | \(\ds X_k \chi_{E_k}\) | Definition of Submartingale |
where $\chi_A$ denotes the characteristic function of $A \subseteq \Omega$.
Therefore:
\(\ds \expect {X_n}\) | \(\ge\) | \(\ds \expect {X_n \chi_E}\) | as $X_n \ge 0$ | |||||||||||
\(\ds \) | \(=\) | \(\ds \sum_{0 \mathop \le k \mathop \le n} \expect {X_n \chi_{E_k} }\) | by $(1)$ | |||||||||||
\(\ds \) | \(=\) | \(\ds \sum_{0 \mathop \le k \mathop \le n} \expect { \expect {X_n \chi_{E_k} \mid \FF_k } }\) | Tower Property of Conditional Expectation | |||||||||||
\(\ds \) | \(\ge\) | \(\ds \sum_{0 \mathop \le k \mathop \le n} \expect { X_k \chi_{E_k} }\) | by $(2)$ | |||||||||||
\(\ds \) | \(\ge\) | \(\ds \sum_{0 \mathop \le k \mathop \le n} \expect { \lambda \chi_{E_k} }\) | as $X_k \ge \lambda$ on $E_k$ | |||||||||||
\(\ds \) | \(=\) | \(\ds \lambda \sum_{0 \mathop \le k \mathop \le n} \expect {\chi_{E_k} }\) | Expectation is Linear | |||||||||||
\(\ds \) | \(=\) | \(\ds \lambda \sum_{0 \mathop \le k \mathop \le n} \map \Pr {E_k}\) | Integral of Characteristic Function | |||||||||||
\(\ds \) | \(=\) | \(\ds \lambda \map \Pr E\) | by $(1)$ |
$\blacksquare$