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201205s2020 xx o ||| 0 eng d |
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|a EBLCP
|b eng
|c EBLCP
|d EBLCP
|d REDDC
|d OCLCF
|d LOA
|d K6U
|d OCLCQ
|d OCLCO
|d OCLCQ
|d DXU
|d OCLCL
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|a 9781119779414
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|a 1119779413
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|a (OCoLC)1225549506
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|a QA276
|b .S738 2020
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|a 519.5
|2 23
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|a UAMI
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| 100 |
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|a Barbu, Vlad Stefan.
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| 245 |
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|a Statistical Topics and Stochastic Models for Dependent Data with Applications
|h [electronic resource] :
|b Applications in Reliability, Survival Analysis and Related Fields.
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| 260 |
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|a Newark :
|b John Wiley & Sons, Incorporated,
|c 2020.
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| 300 |
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|a 1 online resource (281 p.)
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| 500 |
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|a Description based upon print version of record.
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|a Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Part 1 Markov and Semi-Markov Processes -- Chapter 1 Variable Length Markov Chains, Persistent Random Walks: A Close Encounter -- 1.1. Introduction -- 1.2. VLMCs: definition of the model -- 1.3. Definition and behavior of PRWs -- 1.3.1. PRWs in dimension one -- 1.3.2. PRWs in dimension two -- 1.4. VLMC: existence of stationary probability measures -- 1.5. Where VLMC and PRW meet -- 1.5.1. Semi-Markov chains and Markov additive processes -- 1.5.2. PRWs induce semi-Markov chains
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| 505 |
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|a 3.3.2. Two-stage model -- 3.3.3. H model -- 3.3.4. Three-stage model -- 3.3.5. N-stage model -- 3.3.6. Other extensions -- 3.4. Markov chain stock models -- 3.4.1. Hurley and Johnson model -- 3.4.2. Yao model -- 3.4.3. Markov stock model -- 3.4.4. Multivariate Markov chain stock model -- 3.5. Conclusion -- 3.6. References -- Chapter 4 Estimation of Piecewise-deterministic Trajectories in a Quantum Optics Scenario -- 4.1. Introduction -- 4.1.1. The postulates of quantum mechanics -- 4.1.2. Dynamics of open quantum Markovian systems -- 4.1.3. Stochastic wave function: quantum dynamics as PDPs
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|a 4.1.4. Estimation for PDPs -- 4.2. Problem formulation -- 4.2.1. Atom-field interaction -- 4.2.2. Piecewise-deterministic trajectories -- 4.2.3. Measures -- 4.3. Estimation procedure -- 4.3.1. Strategy -- 4.3.2. Least-square estimators -- 4.3.3. Numerical experiments -- 4.4. Physical interpretation -- 4.5. Concluding remarks -- 4.6. References -- Chapter 5 Identification of Patterns in a Semi-Markov Chain -- 5.1. Introduction -- 5.2. The prefix chain -- 5.3. The semi-Markov setting -- 5.4. The hitting time of the pattern -- 5.5. A genomic application -- 5.6. Concluding remarks -- 5.7. References
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|a Part 2 Autoregressive Processes -- Chapter 6 Time Changes and Stationarity Issues for Continuous Time Autoregressive Processes of Order -- 6.1. Introduction -- 6.2. Basics -- 6.3. Stationary AR processes -- 6.3.1. Formulas for the two first-order moments -- 6.3.2. Examples -- 6.3.3. Conditions for stationarity of CAR1(p) processes -- 6.4. Time transforms -- 6.4.1. Properties of time transforms -- 6.4.2. MS processes -- 6.5. Conclusion -- 6.6. Appendix -- 6.7. References -- Chapter 7 Sequential Estimation for Non-parametric Autoregressive Models -- 7.1. Introduction -- 7.2. Main conditions
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| 500 |
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|a 7.3. Pointwise estimation with absolute error risk.
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| 590 |
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|a ProQuest Ebook Central
|b Ebook Central Academic Complete
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| 650 |
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|a Mathematical statistics.
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| 650 |
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7 |
|a Mathematical statistics
|2 fast
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| 700 |
1 |
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|a Vergne, Nicolas.
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| 758 |
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|i has work:
|a Statistical topics and stochastic models for dependent data with applications (Text)
|1 https://id.oclc.org/worldcat/entity/E39PCH8km78qmpFhyfyKQJqDWP
|4 https://id.oclc.org/worldcat/ontology/hasWork
|
| 776 |
0 |
8 |
|i Print version:
|a Barbu, Vlad Stefan
|t Statistical Topics and Stochastic Models for Dependent Data with Applications : Applications in Reliability, Survival Analysis and Related Fields
|d Newark : John Wiley & Sons, Incorporated,c2020
|z 9781786306036
|
| 856 |
4 |
0 |
|u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=6370637
|z Texto completo
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| 880 |
8 |
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|6 505-00/(S
|a 1.5.3. Semi-Markov chain of the α-LIS in a stable VLMC -- 1.5.4. The meeting point -- 1.6. References -- Chapter 2 Bootstraps of Martingale-difference Arrays Under the Uniformly Integrable Entropy -- 2.1. Introduction and motivation -- 2.2. Some preliminaries and notation -- 2.3. Main results -- 2.4. Application for the semi-Markov kernel estimators -- 2.5. Proofs -- 2.6. References -- Chapter 3 A Review of the Dividend Discount Model: From Deterministic to Stochastic Models -- 3.1. Introduction -- 3.2. General model -- 3.3. Gordon growth model and extensions -- 3.3.1. Gordon model
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| 936 |
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|a BATCHLOAD
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| 938 |
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|a ProQuest Ebook Central
|b EBLB
|n EBL6370637
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| 994 |
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|a 92
|b IZTAP
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