[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/wly/apsmbi/v37y2021i1p113-138.html
   My bibliography  Save this article

Cost‐efficient monitoring of continuous‐time stochastic processes based on discrete observations

Author

Listed:
  • Hidekazu Yoshioka
  • Yuta Yaegashi
  • Motoh Tsujimura
  • Yumi Yoshioka
Abstract
Planning a cost‐efficient monitoring policy of stochastic processes arises from many industrial problems. We formulate a simple discrete‐time monitoring problem of continuous‐time stochastic processes with its applications to several industrial problems. A key in our model is a doubling trick of the variables, with which we can construct an algorithm to solve the problem. The cost‐efficient monitoring policy balancing between the observation cost and information loss is governed by an optimality equation of a fixed point type, which is solvable with an iterative algorithm based on the Feynman‐Kac formula. This is a new linkage between monitoring problems and mathematical sciences. We show regularity results of the optimization problem and present a numerical algorithm for its approximation. A problem having model ambiguity is presented as well. The presented model is applied to problems of environment, ecology, and energy, having qualitatively different target stochastic processes with each other.

Suggested Citation

  • Hidekazu Yoshioka & Yuta Yaegashi & Motoh Tsujimura & Yumi Yoshioka, 2021. "Cost‐efficient monitoring of continuous‐time stochastic processes based on discrete observations," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 37(1), pages 113-138, January.
    • Handle: RePEc:wly:apsmbi:v:37:y:2021:i:1:p:113-138
    DOI: 10.1002/asmb.2559
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/asmb.2559
    Download Restriction: no
    File URL: https://libkey.io/10.1002/asmb.2559?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
    ---><---

    References listed on IDEAS

    as
    1. Meyer-Gohde, Alexander, 2019. "Generalized entropy and model uncertainty," Journal of Economic Theory, Elsevier, vol. 183(C), pages 312-343.
    2. Cohen, Samuel N. & Henckel, Timo & Menzies, Gordon D. & Muhle-Karbe, Johannes & Zizzo, Daniel J., 2019. "Switching cost models as hypothesis tests," Economics Letters, Elsevier, vol. 175(C), pages 32-35.
    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. Timo Henckel & Gordon D. Menzies & Peter Moffat & Daniel J. Zizzo, 2019. "Three Dimensions of Central Bank Credibility and Inferential Expectations: The Euro Zone," Working Paper Series 56, Economics Discipline Group, UTS Business School, University of Technology, Sydney.
    2. Timo Henckel & Gordon D. Menzies & Peter G. Moffatt & Daniel J. Zizzo, 2022. "Belief adjustment: a double hurdle model and experimental evidence," Experimental Economics, Springer;Economic Science Association, vol. 25(1), pages 26-67, February.
    3. Dejian Tian, 2022. "Pricing principle via Tsallis relative entropy in incomplete market," Papers 2201.05316, arXiv.org, revised Oct 2022.
    4. Samuel N. Cohen & Derek Snow & Lukasz Szpruch, 2021. "Black-box model risk in finance," Papers 2102.04757, arXiv.org.
    5. Ma, Hanmin & Tian, Dejian, 2021. "Generalized entropic risk measures and related BSDEs," Statistics & Probability Letters, Elsevier, vol. 174(C).
    6. Henckel, Timo & Menzies, Gordon D. & Moffatt, Peter & Zizzo, Daniel J., 2019. "Three dimensions of central bank credibility and inferential expectations: The Euro zone," Journal of Macroeconomics, Elsevier, vol. 60(C), pages 294-308.

    More about this item

    Statistics

    Access and download statistics

    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:wly:apsmbi:v:37:y:2021:i:1:p:113-138. 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: Wiley Content Delivery (email available below). General contact details of provider: https://doi.org/10.1002/(ISSN)1526-4025 .

    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.