Improved likelihood estimation for noisy gamma degradation processes via sequential Monte Carlo-Reference-Cited by-同舟云学术

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Improved likelihood estimation for noisy gamma degradation processes via sequential Monte Carlo

Published:2024-06-06 Issue: Volume: Page:1-25
ISSN:0361-0918
Container-title:Communications in Statistics - Simulation and Computation
language:en
Short-container-title:Communications in Statistics - Simulation and Computation

Author:

Buist Merel¹,Vaisman Radislav²,Vlasiou Maria³

Affiliation:

1. Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, the Netherlands

2. Faculty of Science, The University of Queensland, Brisbane, St Lucia, Queensland, Australia

3. Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Enschede, the Netherlands

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/03610918.2024.2358128

Reference34 articles.

1. A Gamma Wear Process

2. Particle Markov chain Monte Carlo methods

3. Chen, D.-G, and Y. Lio. 2017. Statistical Modeling for Degradation Data. eds. Hon Keung Tony Ng, and Tzong-Ru Tsai. Singapore: Springer.

4. Chopin, N., and O. Papaspiliopoulos. 2020. An introduction to sequential Monte Carlo. Cham, Switzerland: Springer Series in Statistics.

5. Estimating Drug Shelf-Life with Random Batches

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