Chapter 6: Problem 33
Let \(\left\\{X_{n}\right\\}\) be a success runs Markov chain having transition
probabilities \(P_{i, i+1}=p_{i}=1-P_{i, 0}\), for \(i=0,1, \ldots\) Suppose
\(0
Chapter 6: Problem 33
Let \(\left\\{X_{n}\right\\}\) be a success runs Markov chain having transition
probabilities \(P_{i, i+1}=p_{i}=1-P_{i, 0}\), for \(i=0,1, \ldots\) Suppose
\(0
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Get started for freeLet \(\left\\{X_{n}\right\\}\) be a submartingale. Strengthen the maximal inequality, Lemma S.I., to $$ \begin{aligned} \lambda \operatorname{Pr}\left\\{\max _{0 \leq k \leq n} X_{k}>\lambda\right\\} & \leq E\left[X_{n} I\left\\{\max _{0 \leq k \leq n} X_{k}>\lambda\right\\}\right] \\ & \leq E\left[X_{n}^{+}\right] \leq E\left[\left|X_{n}\right|\right], \quad \lambda>0 \end{aligned} $$
Let \(X_{n}\) be the total assets of an insurance company at the end of year \(n\). In each year, \(n\), premiums totaling \(b>0\) are received, and claims \(A_{n}\) are paid, 8o \(X_{n+1}=X_{n}+b-A_{n} .\) Assume \(A_{1}, A_{2}, \ldots\) are independent random variables, each normally distributed with mean \(\mu
Let \(Z, Y_{0}, Y_{1}, \ldots\) be jointly distributed random variables and assume \(E\left[|Z|^{2}\right]<\infty .\) Show that \(X_{n}=E\left[Z \mid Y_{0}, \ldots, Y_{n}\right]\) satisfies the conditions for the martingale mean square convergence theorem.
Let \(\varphi(\xi)\) be a symmetric function, nondecreasing in \(|\xi|\), with \(\varphi(0)=0\), and such that \(\left\\{\varphi\left(\dot{Y}_{j}\right)\right\\}_{j=0}^{n}\) is a submartingale. Fix \(0=u_{0} \leq u_{1} \leq \cdots \leq u_{n}\). Show that $$ \operatorname{Pr}\left\\{\left|Y_{j}\right| \leq u_{j} ; 1 \leq j \leq n\right\\} \geq 1-\sum_{j=1}^{n} \frac{E\left[\varphi\left(Y_{j}\right)\right]-E\left[\varphi\left(Y_{j-1}\right)\right]}{\varphi\left(u_{j}\right)} $$ (If \(\varphi(\xi)=\zeta^{2}, u_{1}=\cdots=u_{n}=\lambda\), we obtain Kolmogorov's inequality.)
Let \(\left\\{U_{n}\right\\}\) and \(\left\\{V_{A}\right\\}\) be martingales with respect to the same process \(\left\\{Y_{n}\right\\}\). Suppose \(U_{0}=V_{0}=0\) and \(E\left[U_{n}^{2}\right]<\infty, E\left[V_{n}^{2}\right]<\infty\) for all \(n\). Show $$ E\left[U_{n} V_{n}\right]=\sum_{k=1}^{n} E\left[\left(U_{k}-U_{k-1}\right)\left(V_{k}-V_{k-1}\right)\right] $$ As a special case, $$ E\left[U_{n}^{2}\right]=\sum_{k=1}^{n} E\left[\left(U_{k}-U_{k-1}\right)^{2}\right] $$
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