Nothing Special   »   [go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v83y2013i9p2077-2080.html
   My bibliography  Save this article

Tuned iterated filtering

Author

Listed:
  • Lindström, Erik
Abstract
Iterated filtering is an algorithm for estimating parameters in partially observed Markov process (POMP) models. The real-world performance of the algorithm depends on several tuning parameters. We propose a simple method for optimizing the parameter governing the joint dynamics of the hidden parameter process (called the Σ matrix).

Suggested Citation

  • Lindström, Erik, 2013. "Tuned iterated filtering," Statistics & Probability Letters, Elsevier, vol. 83(9), pages 2077-2080.
    • Handle: RePEc:eee:stapro:v:83:y:2013:i:9:p:2077-2080
    DOI: 10.1016/j.spl.2013.05.019
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715213001806
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2013.05.019?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Aaron A. King & Edward L. Ionides & Mercedes Pascual & Menno J. Bouma, 2008. "Inapparent infections and cholera dynamics," Nature, Nature, vol. 454(7206), pages 877-880, August.
    2. Lindström, Erik & Ströjby, Jonas & Brodén, Mats & Wiktorsson, Magnus & Holst, Jan, 2008. "Sequential calibration of options," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 2877-2891, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bardgett, Chris & Gourier, Elise & Leippold, Markus, 2019. "Inferring volatility dynamics and risk premia from the S&P 500 and VIX markets," Journal of Financial Economics, Elsevier, vol. 131(3), pages 593-618.
    2. Piotr Szczepocki, 2020. "Application of iterated filtering to stochastic volatility models based on non-Gaussian Ornstein-Uhlenbeck process," Statistics in Transition New Series, Polish Statistical Association, vol. 21(2), pages 173-187, June.
    3. Leippold, Markus & Vasiljević, Nikola, 2017. "Pricing and disentanglement of American puts in the hyper-exponential jump-diffusion model," Journal of Banking & Finance, Elsevier, vol. 77(C), pages 78-94.
    4. David R J Pleydell & Samuel Soubeyrand & Sylvie Dallot & Gérard Labonne & Joël Chadœuf & Emmanuel Jacquot & Gaël Thébaud, 2018. "Estimation of the dispersal distances of an aphid-borne virus in a patchy landscape," PLOS Computational Biology, Public Library of Science, vol. 14(4), pages 1-24, April.
    5. Ross Sparks & Tim Keighley & David Muscatello, 2010. "Early warning CUSUM plans for surveillance of negative binomial daily disease counts," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(11), pages 1911-1929.
    6. Liu, Qun & Jiang, Daqing & Hayat, Tasawar & Alsaedi, Ahmed & Ahmad, Bashir, 2020. "Stationary distribution of a stochastic cholera model between communities linked by migration," Applied Mathematics and Computation, Elsevier, vol. 373(C).
    7. Cristian Homescu, 2011. "Implied Volatility Surface: Construction Methodologies and Characteristics," Papers 1107.1834, arXiv.org.
    8. Gabriel Kolaye Guilsou & Moulay-Ahmed Aziz-Alaoui & Raymond Houé Ngouna & Bernard Archimede & Samuel Bowong, 2023. "Gaining Profound Knowledge of Cholera Outbreak: The Significance of the Allee Effect on Bacterial Population Growth and Its Implications for Human-Environment Health," Sustainability, MDPI, vol. 15(13), pages 1-30, June.
    9. David A Rasmussen & Oliver Ratmann & Katia Koelle, 2011. "Inference for Nonlinear Epidemiological Models Using Genealogies and Time Series," PLOS Computational Biology, Public Library of Science, vol. 7(8), pages 1-11, August.
    10. Fig-Talamanca, Gianna, 2009. "Testing volatility autocorrelation in the constant elasticity of variance stochastic volatility model," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2201-2218, April.
    11. Ross, J.V. & Pagendam, D.E. & Pollett, P.K., 2009. "On parameter estimation in population models II: Multi-dimensional processes and transient dynamics," Theoretical Population Biology, Elsevier, vol. 75(2), pages 123-132.
    12. Pascal François & Lars Stentoft, 2021. "Smile‐implied hedging with volatility risk," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 41(8), pages 1220-1240, August.
    13. Yohana Maiga Marwa & Isambi Sailon Mbalawata & Samuel Mwalili, 2019. "Continuous Time Markov Chain Model for Cholera Epidemic Transmission Dynamics," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 8(3), pages 1-32, November.
    14. Lili Zhuang & Noel Cressie, 2014. "Bayesian hierarchical statistical SIRS models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 23(4), pages 601-646, November.
    15. Emese Lazar & Shuyuan Qi & Radu Tunaru, 2020. "Measures of Model Risk in Continuous-time Finance Models," Papers 2010.08113, arXiv.org, revised Oct 2020.
    16. Cai, Li-Ming & Modnak, Chairat & Wang, Jin, 2017. "An age-structured model for cholera control with vaccination," Applied Mathematics and Computation, Elsevier, vol. 299(C), pages 127-140.
    17. Tobias S Brett & Eamon B O’Dea & Éric Marty & Paige B Miller & Andrew W Park & John M Drake & Pejman Rohani, 2018. "Anticipating epidemic transitions with imperfect data," PLOS Computational Biology, Public Library of Science, vol. 14(6), pages 1-18, June.
    18. Qi, Haokun & Meng, Xinzhu, 2021. "Mathematical modeling, analysis and numerical simulation of HIV: The influence of stochastic environmental fluctuations on dynamics," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 187(C), pages 700-719.
    19. Josef Danv{e}k & J. Posp'iv{s}il, 2020. "Numerical aspects of integration in semi-closed option pricing formulas for stochastic volatility jump diffusion models," Papers 2006.13181, arXiv.org.
    20. King, Aaron A. & Nguyen, Dao & Ionides, Edward L., 2016. "Statistical Inference for Partially Observed Markov Processes via the R Package pomp," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 69(i12).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:stapro:v:83:y:2013:i:9:p:2077-2080. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.