A non-parametric Bayesian approach to decompounding from high frequency data-Reference-Cited by-同舟云学术

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A non-parametric Bayesian approach to decompounding from high frequency data

Published:2016-12-16 Issue:1 Volume:21 Page:53-79
ISSN:1387-0874
Container-title:Statistical Inference for Stochastic Processes
language:en
Short-container-title:Stat Inference Stoch Process

Author:

Gugushvili Shota,van der Meulen Frank,Spreij Peter

Funder

Delft University of Technology

Publisher

Springer Science and Business Media LLC

Subject

Statistics and Probability

Link

http://link.springer.com/article/10.1007/s11203-016-9153-1/fulltext.html

Reference46 articles.

1. Alexandersson H (1985) A simple stochastic model of the precipitation process. J Clim Appl Meteorol 24(12):1282–1295

2. Belomestny D, Comte F, Genon-Catalot V, Masuki H, Reiß M (eds) (2015) Lévy matters IV, estimation for discretely observed Lévy processes. Lecture notes in mathematics 2128. Springer, Cham

3. Birgé L (1984) Sur un théorème de minimax et son application aux tests. Probab Math Stat 3:259–282

4. Brooks S, Gelman A, Jones GL, Meng XL (2011) Handbook of Markov chain Monte Carlo. Chapman and Hall/CRC, Hoboken

5. Buchmann B, Grübel R (2003) Decompounding: an estimation problem for Poisson random sums. Ann Stat 31:1054–1074

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