IDEAS home Printed from https://ideas.repec.org/a/spr/sankha/v86y2024i1d10.1007_s13171-024-00341-1.html
   My bibliography  Save this article

A Censored Time Series Analysis for Responses on the Unit Interval: An Application to Acid Rain Modeling

Author

Listed:
  • Fernanda L. Schumacher

    (The Ohio State University)

  • Larissa A. Matos

    (Universidade Estadual de Campinas)

  • Víctor H. Lachos

    (University of Connecticut)

  • Carlos A. Abanto-Valle

    (Federal University of Rio de Janeiro)

  • Luis M. Castro

    (Pontificia Universidad Católica de Chile
    Center for the Discovery of Structures in Complex Data)

Abstract

In this paper, we propose an autoregressive model for time series in which the variable of interest lies in the unit interval and is subject to certain threshold values below or above which the measurements are not quantifiable. The model includes an independent beta regression (Ferrari and Cribari-Neto, J. Appl. Stat., 31, 799–815 2004) as a special case. A Markov chain Monte Carlo (MCMC) algorithm is tailored to obtain Bayesian posterior distributions of unknown quantities of interest. The likelihood function was used to compute Bayesian model selection measures. We discuss the construction of the proposed model and compare it with alternative models by using simulated data. Finally, we illustrate the use of our proposal by modeling a left-censored weekly series of acid rain data.

Suggested Citation

  • Fernanda L. Schumacher & Larissa A. Matos & Víctor H. Lachos & Carlos A. Abanto-Valle & Luis M. Castro, 2024. "A Censored Time Series Analysis for Responses on the Unit Interval: An Application to Acid Rain Modeling," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 86(1), pages 637-660, February.
    • Handle: RePEc:spr:sankha:v:86:y:2024:i:1:d:10.1007_s13171-024-00341-1
    DOI: 10.1007/s13171-024-00341-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s13171-024-00341-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s13171-024-00341-1?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.

    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:spr:sankha:v:86:y:2024:i:1:d:10.1007_s13171-024-00341-1. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.