Statistical topics and stochastic models for dependent data with applications : applications in reliability, survival analysis and related fields
Published
London : Hoboken : ISTE, Ltd. ; Wiley, 2020.
Status
Available Online
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Language
English
ISBN
9781119779421, 1119779421, 9781119779414, 1119779413
Notes
General Note
7.3. Pointwise estimation with absolute error risk.
Description
This book is a collective volume authored by leading scientists in the field of stochastic modelling, associated statistical topics and corresponding applications. The main classes of stochastic processes for dependent data investigated throughout this book are Markov, semi-Markov, autoregressive and piecewise deterministic Markov models. The material is divided into three parts corresponding to: (i) Markov and semi-Markov processes, (ii) autoregressive processes and (iii) techniques based on divergence measures and entropies. A special attention is payed to applications in reliability, survival analysis and related fields.
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O'Reilly,O'Reilly Online Learning: Academic/Public Library Edition
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Citations
APA Citation, 7th Edition (style guide)
Barbu, V. S., & Vergne, N. (2020). Statistical topics and stochastic models for dependent data with applications: applications in reliability, survival analysis and related fields . ISTE, Ltd. ; Wiley.
Chicago / Turabian - Author Date Citation, 17th Edition (style guide)Barbu, Vlad Stefan and Nicolas. Vergne. 2020. Statistical Topics and Stochastic Models for Dependent Data With Applications: Applications in Reliability, Survival Analysis and Related Fields. London : Hoboken: ISTE, Ltd. ; Wiley.
Chicago / Turabian - Humanities (Notes and Bibliography) Citation, 17th Edition (style guide)Barbu, Vlad Stefan and Nicolas. Vergne. Statistical Topics and Stochastic Models for Dependent Data With Applications: Applications in Reliability, Survival Analysis and Related Fields London : Hoboken: ISTE, Ltd. ; Wiley, 2020.
Harvard Citation (style guide)Barbu, V. S. and Vergne, N. (2020). Statistical topics and stochastic models for dependent data with applications: applications in reliability, survival analysis and related fields. London : Hoboken: ISTE, Ltd. ; Wiley.
MLA Citation, 9th Edition (style guide)Barbu, Vlad Stefan., and Nicolas Vergne. Statistical Topics and Stochastic Models for Dependent Data With Applications: Applications in Reliability, Survival Analysis and Related Fields ISTE, Ltd. ; Wiley, 2020.
Note! Citations contain only title, author, edition, publisher, and year published. Citations should be used as a guideline and should be double checked for accuracy. Citation formats are based on standards as of August 2021.
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Grouped Work ID
a2bc4a69-d124-9dc0-6c4b-39c535b43ca2-eng
Grouping Information
| Grouped Work ID | a2bc4a69-d124-9dc0-6c4b-39c535b43ca2-eng |
|---|---|
| Full title | statistical topics and stochastic models for dependent data with applications applications in reliability survival analysis and related fields |
| Author | vlad stefan barbu nicolas vergne |
| Grouping Category | book |
| Last Update | 2024-12-17 08:40:50AM |
| Last Indexed | 2024-12-17 08:42:37AM |
Book Cover Information
| Image Source | default |
|---|---|
| First Loaded | Aug 14, 2023 |
| Last Used | Nov 10, 2024 |
Marc Record
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| Last File Modification Time | Dec 17, 2024 08:13:30 AM |
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| 245 | 0 | 0 | |a Statistical topics and stochastic models for dependent data with applications :|b applications in reliability, survival analysis and related fields /|c edited by Vlad Stefan Barbu, Nicolas Vergne. |
| 260 | |a London :|b ISTE, Ltd. ;|a Hoboken :|b Wiley,|c 2020. | ||
| 300 | |a 1 online resource (281 pages) | ||
| 336 | |a text|b txt|2 rdacontent | ||
| 337 | |a computer|b c|2 rdamedia | ||
| 338 | |a online resource|b cr|2 rdacarrier | ||
| 490 | 1 | |a Mathematics and statistics | |
| 500 | |a 7.3. Pointwise estimation with absolute error risk. | ||
| 505 | 0 | |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 | |
| 505 | 8 | |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 | |
| 505 | 8 | |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 | |
| 505 | 8 | |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 | |
| 520 | |a This book is a collective volume authored by leading scientists in the field of stochastic modelling, associated statistical topics and corresponding applications. The main classes of stochastic processes for dependent data investigated throughout this book are Markov, semi-Markov, autoregressive and piecewise deterministic Markov models. The material is divided into three parts corresponding to: (i) Markov and semi-Markov processes, (ii) autoregressive processes and (iii) techniques based on divergence measures and entropies. A special attention is payed to applications in reliability, survival analysis and related fields. | ||
| 588 | 0 | |a Print version record. | |
| 590 | |a O'Reilly|b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Mathematical statistics.|9 46678 | |
| 650 | 0 | |a Stochastic processes.|9 56796 | |
| 700 | 1 | |a Barbu, Vlad Stefan. | |
| 700 | 1 | |a Vergne, Nicolas. | |
| 758 | |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, ©2020|z 9781786306036 |
| 830 | 0 | |a Mathematics and statistics series (ISTE) | |
| 856 | 4 | 0 | |u https://library.access.arlingtonva.us/login?url=https://learning.oreilly.com/library/view/~/9781786306036/?ar|x O'Reilly|z eBook |
| 880 | 8 | |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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