The balanced discrete triplet Lindley model and its INAR(1) extension: properties and COVID-19 applications

Shirozhan Masoumeh (), Mamode Khan Naushad A. () and Kokonendji Célestin C. ()
Additional contact information
Shirozhan Masoumeh: Water and Wastewater Company, Ardabil, Ardabil Province, Iran
Mamode Khan Naushad A.: Department of Economics and Statistics, University of Mauritius, Moka, Mauritius
Kokonendji Célestin C.: Laboratoire de Mathématiques de Besançon UMR 6623 CNRS-UBFC, Université Bourgogne Franche-Comté, Besançon, France

The International Journal of Biostatistics, 2023, vol. 19, issue 2, 489-516

Abstract: This paper proposes a new flexible discrete triplet Lindley model that is constructed from the balanced discretization principle of the extended Lindley distribution. This model has several appealing statistical properties in terms of providing exact and closed form moment expressions and handling all forms of dispersion. Due to these, this paper explores further the usage of the discrete triplet Lindley as an innovation distribution in the simple integer-valued autoregressive process (INAR(1)). This subsequently allows for the modeling of count time series observations. In this context, a novel INAR(1) process is developed under mixed Binomial and the Pegram thinning operators. The model parameters of the INAR(1) process are estimated using the conditional maximum likelihood and Yule-Walker approaches. Some Monte Carlo simulation experiments are executed to assess the consistency of the estimators under the two estimation approaches. Interestingly, the proposed INAR(1) process is applied to analyze the COVID-19 cases and death series of different countries where it yields reliable parameter estimates and suitable forecasts via the modified Sieve bootstrap technique. On the other side, the new INAR(1) with discrete triplet Lindley innovations competes comfortably with other established INAR(1)s in the literature.

Keywords: conditional maximum likelihood; COVID-19; INAR(1); sieve bootstrap; triplet Lindley (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/ijb-2022-0001 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:ijbist:v:19:y:2023:i:2:p:489-516:n:8

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/ijb/html

DOI: 10.1515/ijb-2022-0001

Access Statistics for this article

The International Journal of Biostatistics is currently edited by Antoine Chambaz, Alan E. Hubbard and Mark J. van der Laan

More articles in The International Journal of Biostatistics from De Gruyter
Bibliographic data for series maintained by Peter Golla ().