IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v47y2020i3p524-540.html
   My bibliography  Save this article

Analysis of batched service time data using Gaussian and semi-parametric kernel models

Author

Listed:
  • Xueying Wang
  • Chunxiao Zhou
  • Kepher Makambi
  • Ao Yuan
  • Jaeil Ahn

Abstract

Batched data is a type of data where each observed data value is the sum of a number of grouped (batched) latent ones obtained under different conditions. Batched data arises in various practical backgrounds and is often found in social studies and management sector. The analysis of such data is analytically challenging due to its structural complexity. In this article, we describe how to analyze batched service time data, estimate the mean and variance of each batch that are latent. We in particular focus on the situation when the observed total time includes an unknown proportion of non-service time. To address this problem, we propose a Gaussian model for efficiency as well as a semi-parametric kernel density model for robustness. We evaluate the performance of both proposed methods through simulation studies and then applied our methods to analyze a batched data.

Suggested Citation

  • Xueying Wang & Chunxiao Zhou & Kepher Makambi & Ao Yuan & Jaeil Ahn, 2020. "Analysis of batched service time data using Gaussian and semi-parametric kernel models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 47(3), pages 524-540, February.
    • Handle: RePEc:taf:japsta:v:47:y:2020:i:3:p:524-540
    DOI: 10.1080/02664763.2019.1645820
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2019.1645820
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2019.1645820?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.

    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:taf:japsta:v:47:y:2020:i:3:p:524-540. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    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.