IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i22p4362-d978511.html
   My bibliography  Save this article

A Look at the Primary Order Preserving Properties of Stochastic Orders: Theorems, Counterexamples and Applications in Cognitive Psychology

Author

Listed:
  • Mohsen Soltanifar

    (Biostatistics Division, Dalla Lana School of Public Health, University of Toronto, 620-155 College Street, Toronto, ON M5T 3M7, Canada
    Analytics Division, College of Professional Studies, Northeastern University, 1400-410 West Georgia Street, Vancouver, BC V6B 1Z3, Canada
    Continuing Studies Division, Population Data BC, University of Victoria, B364-3800 Finnerty Road, Victoria, BC V8P 5C2, Canada
    This paper is an extended version of our paper published in “ Soltanifar, M. Some Results on the Additivity and Multiplication Order Preserving Properties of Stochastic Orders. In JSM Proceedings ; SSC Section; American Statistical Association: Alexandria, VA, USA, 2020; pp. 476–483”.)

Abstract

In this paper, we prove that for a set of ten univariate stochastic orders including the usual order, a univariate stochastic order preserves either both, one or none of additivity and multiplication properties over the vector space of real-valued random variables. Then, classifying participant’s quickness in a mental chronometry trial to “weakly faster” and “strongly faster”, we use the above results for the usual stochastic order to establish necessary and sufficient conditions for a participant to be strongly faster than the other in terms of the fitted Wald, Exponentially modified Wald(ExW), and Exponentially modified Gaussian(ExG) distributional parameters. This research field remains uncultivated for other univariate stochastic orders and in several directions.

Suggested Citation

  • Mohsen Soltanifar, 2022. "A Look at the Primary Order Preserving Properties of Stochastic Orders: Theorems, Counterexamples and Applications in Cognitive Psychology," Mathematics, MDPI, vol. 10(22), pages 1-13, November.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:22:p:4362-:d:978511
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/22/4362/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/22/4362/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mohsen Soltanifar & Chel Hee Lee, 2023. "SimSST: An R Statistical Software Package to Simulate Stop Signal Task Data," Mathematics, MDPI, vol. 11(3), pages 1-15, January.

    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:gam:jmathe:v:10:y:2022:i:22:p:4362-:d:978511. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.