scholarly journals Another look at Bayes map iterated filtering

2016 ◽  
Vol 118 ◽  
pp. 32-36 ◽  
Author(s):  
Dao Nguyen
Keyword(s):  
Iterated Filtering

Tuned iterated filtering

2013 ◽  
Vol 83 (9) ◽  
pp. 2077-2080 ◽  
Author(s):  
Erik Lindström
Keyword(s):  
Iterated Filtering

Efficient Iterated Filtering

2012 ◽  
Vol 45 (16) ◽  
pp. 1785-1790 ◽  
Author(s):  
Erik Lindström ◽  
Edward Ionides ◽  
Jan Frydendall ◽  
Henrik Madsen
Keyword(s):  
Iterated Filtering

scholarly journals Iterated filtering

2011 ◽  
Vol 39 (3) ◽  
pp. 1776-1802 ◽  
Author(s):  
Edward L. Ionides ◽  
Anindya Bhadra ◽  
Yves Atchadé ◽  
Aaron King
Keyword(s):  
Iterated Filtering

scholarly journals Model selection and parameter estimation for dynamic epidemic models via iterated filtering: application to rotavirus in Germany

Biostatistics ◽  
2018 ◽  
Vol 21 (3) ◽  
pp. 400-416 ◽  
Author(s):  
Theresa Stocks ◽  
Tom Britton ◽  
Michael Höhle
Keyword(s):  
Model Selection ◽  
Dynamic Models ◽  
Selection Process ◽  
R Package ◽  
Epidemic Models ◽  
Process Models ◽  
Transmission Model ◽  
Disease Epidemiology ◽  
Partially Observed ◽  
Iterated Filtering

Summary Despite the wide application of dynamic models in infectious disease epidemiology, the particular modeling of variability in the different model components is often subjective rather than the result of a thorough model selection process. This is in part because inference for a stochastic transmission model can be difficult since the likelihood is often intractable due to partial observability. In this work, we address the question of adequate inclusion of variability by demonstrating a systematic approach for model selection and parameter inference for dynamic epidemic models. For this, we perform inference for six partially observed Markov process models, which assume the same underlying transmission dynamics, but differ with respect to the amount of variability they allow for. The inference framework for the stochastic transmission models is provided by iterated filtering methods, which are readily implemented in the R package pomp by King and others (2016, Statistical inference for partially observed Markov processes via the R package pomp. Journal of Statistical Software69, 1–43). We illustrate our approach on German rotavirus surveillance data from 2001 to 2008, discuss practical difficulties of the methods used and calculate a model based estimate for the basic reproduction number $R_0$ using these data.

Application of Iterated Filtering for Parametric Estimation of Instantaneous Variance in the Case of Non-Gaussian Ornstein-Uhlenbeck Stochastic Volatility Processes

2019 ◽  
Vol 66 (1) ◽  
pp. 51-68
Author(s):  
Piotr Szczepocki
Keyword(s):  
Stochastic Volatility ◽  
Simulation Study ◽  
Parametric Estimation ◽  
Estimation Technique ◽  
Variance Estimator ◽  
Realized Variance ◽  
Empirical Application ◽  
Non Gaussian ◽  
Stochastic Volatility Process ◽  
Iterated Filtering

The article presents a method for parametric estimation of instantaneous variance in the case of non-Gaussian Ornstein-Uhlenbeck stochastic volatility process by means of the iterated filtering and realized variance estimator. The method is applied to realized variance of S&P500 index data. Empirical application is accompanied with simulation study to examine performance of the estimation technique.

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