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world:useful-bounds [2022/11/17 17:34] rdouc [Doob's inequalities] |
world:useful-bounds [2022/11/17 21:21] rdouc [Maximal Kolmogorov inequality] |
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==== Maximal Kolmogorov inequality ==== | ==== Maximal Kolmogorov inequality ==== | ||
+ | <WRAP center round tip 80%> | ||
Let \((M_k)_{k\in\nset}\) be a square integrable \((\mcf_k)_{k\in\nset}\)-martingale. Then, | Let \((M_k)_{k\in\nset}\) be a square integrable \((\mcf_k)_{k\in\nset}\)-martingale. Then, | ||
\begin{equation} | \begin{equation} | ||
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\end{equation} | \end{equation} | ||
- | $\bproof$ | + | </WRAP> |
+ | $\bproof$ | ||
+ | <note important> | ||
+ | We provide a complete proof here but actually, we can also apply Doob's inequality below to the non-negative submartingale $M_n^2$ | ||
+ | </note> | ||
Let \(\sigma=\inf\set{k\geq 1}{|M_k| \geq \alpha}\) with the convention that $\inf \emptyset=\infty$. | Let \(\sigma=\inf\set{k\geq 1}{|M_k| \geq \alpha}\) with the convention that $\inf \emptyset=\infty$. | ||
Then, | Then, | ||
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\end{align} | \end{align} | ||
- | We prove **(i)**. From \eqref{eq:fond}, we have | + | * We prove **(i)**. From \eqref{eq:fond}, we have |
$$ | $$ | ||
\epsilon \PP\lr{\max_{k=1}^n X_k \geq \epsilon} \leq \PE[X_{\tau_\epsilon} \indiacc{\tau_\epsilon \leq n}] \leq \PE[X_{\tau_\epsilon \wedge n}]\leq \PE[X_{\tau_\epsilon \wedge 0}]=\PE[X_0] | \epsilon \PP\lr{\max_{k=1}^n X_k \geq \epsilon} \leq \PE[X_{\tau_\epsilon} \indiacc{\tau_\epsilon \leq n}] \leq \PE[X_{\tau_\epsilon \wedge n}]\leq \PE[X_{\tau_\epsilon \wedge 0}]=\PE[X_0] | ||
$$ | $$ | ||
- | where we used that $(X_{\tau_\epsilon \wedge n})$ is an integrable super-martingale. | + | where we used in the second inequality that $(X_n)$ is non-negative and in the third inequality that $(X_{\tau_\epsilon \wedge n})$ is a supermartingale. |
- | We prove **(ii)**. Using \eqref{eq:fond}, | + | * We now turn to the proof of **(ii)**. Using \eqref{eq:fond}, |
\begin{align*} | \begin{align*} | ||
\epsilon \PP\lr{\max_{k=1}^n X_k \geq \epsilon}&\leq \PE[X_{\tau_\epsilon} \indiacc{\tau_\epsilon \leq n}]=\sum_{k=0}^n \PE[X_k \indiacc{\tau_\epsilon=k}]\\ | \epsilon \PP\lr{\max_{k=1}^n X_k \geq \epsilon}&\leq \PE[X_{\tau_\epsilon} \indiacc{\tau_\epsilon \leq n}]=\sum_{k=0}^n \PE[X_k \indiacc{\tau_\epsilon=k}]\\ | ||
&\leq \sum_{k=0}^n \PE[\PE[X_n|\mcf_k] \indiacc{\tau_\epsilon=k}]=\sum_{k=0}^n \PE[X_n \indiacc{\tau_\epsilon=k}]=\PE[X_n \indiacc{\tau_\epsilon\leq n}] \leq \PE[X_n] | &\leq \sum_{k=0}^n \PE[\PE[X_n|\mcf_k] \indiacc{\tau_\epsilon=k}]=\sum_{k=0}^n \PE[X_n \indiacc{\tau_\epsilon=k}]=\PE[X_n \indiacc{\tau_\epsilon\leq n}] \leq \PE[X_n] | ||
\end{align*} | \end{align*} | ||
- | which completes the proof. | + | where we used in the last inequality that $(X_n)$ is non-negative. The proof is completed. |
$\eproof$ | $\eproof$ |