Large deviations for additive functionals of Markov chains / [electronic resource] Alejandro D. de Acosta, Peter Ney.

By: Acosta, Alejandro D. de, 1941- [author.]Contributor(s): Ney, Peter, 1930- [author.]Material type: TextTextSeries: Memoirs of the American Mathematical Society ; v. 1070Publisher: Providence, Rhode Island : American Mathematical Society, [2013]Description: 1 online resource (v, 108 pages : illustrations)Content type: text Media type: unmediated Carrier type: volumeISBN: 9781470414825 (online)Subject(s): Large deviations | Markov processes | Additive functionsAdditional physical formats: Large deviations for additive functionals of Markov chains /DDC classification: 519.2/33 LOC classification: QA273.67 | .A26 2013Online resources: Contents | Contents
Contents:
Chapter 1. Introduction Chapter 2. The transform kernels $K_{g}$ and their convergence parameters Chapter 3. Comparison of $\Lambda (g)$ and $\phi _\mu (g)$ Chapter 4. Proof of Theorem 1 Chapter 5. A characteristic equation and the analyticity of $\Lambda _f$: the case when $P$ has an atom $C\in \mathcal {S}^+$ satisfying $\lambda ^*(C)>0$ Chapter 6. Characteristic equations and the analyticity of $\Lambda _f$: the general case when $P$ is geometrically ergodic Chapter 7. Differentiation formulas for $u_g$ and $\Lambda _f$ in the general case and their consequences Chapter 8. Proof of Theorem 2 Chapter 9. Proof of Theorem 3 Chapter 10. Examples Chapter 11. Applications to an autoregressive process and to reflected random walk Appendix Background comments
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Link to resource Available EBK13523

"March 2014, volume 228, number 1070 (second of 5 numbers)."

Includes bibliographical references (pages 107-108).

Chapter 1. Introduction Chapter 2. The transform kernels $K_{g}$ and their convergence parameters Chapter 3. Comparison of $\Lambda (g)$ and $\phi _\mu (g)$ Chapter 4. Proof of Theorem 1 Chapter 5. A characteristic equation and the analyticity of $\Lambda _f$: the case when $P$ has an atom $C\in \mathcal {S}^+$ satisfying $\lambda ^*(C)>0$ Chapter 6. Characteristic equations and the analyticity of $\Lambda _f$: the general case when $P$ is geometrically ergodic Chapter 7. Differentiation formulas for $u_g$ and $\Lambda _f$ in the general case and their consequences Chapter 8. Proof of Theorem 2 Chapter 9. Proof of Theorem 3 Chapter 10. Examples Chapter 11. Applications to an autoregressive process and to reflected random walk Appendix Background comments

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Electronic reproduction. Providence, Rhode Island : American Mathematical Society. 2014

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