In the published article reference 14 was not cited in the article and an additional citation for reference 11 was missed. The citations have now been inserted in Material and Methods, Degenerate Beta Autoregressive(DeβAR)model, Parameter estimation and should read:
“Here, let if xtϵ(0, 1) else (11, 14) and , where ψ(.) is a digamma function.”
In the published article, there was an error. Inbetween steps of likelihood derivation was missed.
A correction has been made to Material and Methods, Degenerate Beta Autoregressive(DeβAR)model, Parameter estimation. “This sentence previously stated:”
The likelihood function for the parameters of Degenerate Beta AR model is given by,
“The corrected sentence appears below:”
where,
The likelihood function for the parameters of Degenerate Beta AR model is given by,
In the published article, there was an error. Limitation of the model has been added and reference 19 has been added.
A correction has been made to Material and Methods, Degenerate Beta Autoregressive(DeβAR)model, Parameter estimation. “This sentence previously stated:”
Large sample inference: If the model specified by Equation (5) follows the regularity condition of maximum likelihood estimation (MLE) then, MLEs of θ and J(θ) (Fisher information matrix) are consistent. Assuming that exists and is non-singular, we have converges in distribution to N(0, I(θ)−1).
“The corrected sentence appears below:”
Large sample inference: If the model specified by Equation (5) follows the regularity condition of maximum likelihood estimation (MLE) then, MLE of θ and J(θ) (Fisher information matrix) are consistent. Assuming that exists and is nonsingular, we have converges in distribution to N(0, I(θ)−1).
Note: The proposed DeβAR model is applicable when is converted to 0 as mentioned above. To overcome with this limitation Bayer et al. (19) proposed Inflated beta autoregressive moving average models, which are more suitable when interval data includes 0 or 1.
The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
Statements
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
11.
OspinaRFerrariSL. A general class of zero-or-one inflated beta regression models. Comput Stat Data Anal. (2012) 56:1609–23. 10.1016/j.csda.2011.10.005
14.
BenjaminMARigbyRAStasinopoulosMD. Fitting non-Gaussian time series models. In: InCOMPSTAT: Proceedings in Computational Statistics 13th Symposium held in Bristol, Great Britain, 1998. (1998). p. 191–6.
19.
BayerFMPumiGPereiraTLSouzaTC. Inflated beta autoregressive moving average models. Comput Appl Math. (2023) 42:183. 10.1007/s40314-023-02322-w
Summary
Keywords
Beta distribution, time-series model, mixture distribution, rates, proportions, inflated distribution, AMR, resistance
Citation
Lobo J, Kamath A and Kalwaje Eshwara V (2024) Corrigendum: Degenerate Beta autoregressive model for proportion time-series with zeros or ones: an application to antimicrobial resistance rate using R shiny app. Front. Public Health 12:1415866. doi: 10.3389/fpubh.2024.1415866
Received
11 April 2024
Accepted
18 April 2024
Published
16 May 2024
Volume
12 - 2024
Edited and reviewed by
Marwan Osman, Yale University, United States
Updates
Copyright
© 2024 Lobo, Kamath and Kalwaje Eshwara.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Asha Kamath asha.kamath@manipal.edu
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.