Multistate Models, Flowgraph Models, and Semi-Markov Processes
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Publication:3155271
DOI10.1081/STA-120028678zbMath1066.62103MaRDI QIDQ3155271
Publication date: 14 January 2005
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Applications of statistics to biology and medical sciences; meta analysis (62P10) Applications of graph theory (05C90) Survival analysis and censored data (62N99) Estimation in survival analysis and censored data (62N02) Applications of Markov renewal processes (reliability, queueing networks, etc.) (60K20)
Related Items (7)
A semi-Markov multistate model for estimation of the mean quality-adjusted survival for non-progressive processes ⋮ Flowgraph models for estimating total time of authentication process in RFID system ⋮ Saddlepoint approximation for semi-Markov processes with application to a cardiovascular randomised study ⋮ A Censored Data Histogram ⋮ Unnamed Item ⋮ Survival analysis in supply chains using statistical flowgraph models: Predicting time to supply chain disruption ⋮ Towards a unified view of finite automata and semi-Markov flowgraph models
Cites Work
- Multi-state models: A review
- Survival analysis. Techniques for censored and truncated data
- Modeling survival data: extending the Cox model
- Nonparametric reliability estimation of semi-Markov processes
- Throughput availability in Markov systems
- A Stochastic Model for Censored-Survival Data in the Presence of an Auxiliary Variable
- Flowgraph Models for Generalized Phase Type Distributions Having Non-Exponential Waiting Times
- Stochastic Network Models for Survival Analysis
- Survival and Hazard Functions for Progressive Diseases Using Saddlepoint Approximations
- Multi-state models for event history analysis
- Analysis of repeated events
- Competing risks as a multi-state model
- Saddlepoint Approximations in Statistics
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