
==== Front
PLoS One
PLoS One
plos
PLOS ONE
1932-6203
Public Library of Science San Francisco, CA USA

10.1371/journal.pone.0310870
PONE-D-24-39986
Correction
Correction: Memory-type variance estimators using exponentially weighted moving average statistic in presence of measurement error for time-scaled surveys
Qureshi Muhammad Nouman
Alamri Osama Abdulaziz
Riaz Naureen
Iftikhar Ayesha
Tariq Muhammad Umair
Hanif Muhammad
17 9 2024
2024
17 9 2024
19 9 e0310870© 2024 Qureshi et al
2024
Qureshi et al
https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Memory-Type Variance Estimators using Exponentially Weighted Moving Average Statistic in Presence of Measurement Error for Time-Scaled Surveys
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pmcAn additional affiliation is missing for the first author. Muhammad Nouman Qureshi is also affiliated with National College of Business Administration and Economics in Lahore, Pakistan.

After this article [1] was published, the authors also made the following clarifications:

For the real data for the true consumption expenditure study, the dataset found on page 485, Chapter 13, Example 13.2 of the book Basic Econometrics, 4th Edition, by Gujarati [2] was used.

Multiple Indicator Cluster Surveys (MICS) is mentioned in the first paragraph of the Introduction in [1] as it provides context and rationale for the study. However, for the empirical analysis presented in [1], the MICS data was not directly utilized.
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References

1 Qureshi MN , Alamri OA , Riaz N , Iftikhar A , Tariq MU , Hanif M (2023) Memory-type variance estimators using exponentially weighted moving average statistic in presence of measurement error for time-scaled surveys. PLoS ONE 18 (11 ): e0277697. 10.1371/journal.pone.0277697 37944483
2 Gujarati D N. Basic Econometrics (Fourth Edi). The McGraw−Hill Companies. 2004
