On some robust imputation methods in presence of correlated measurement errors with real data applications Article Swipe
YOU?
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· 2024
· Open Access
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· DOI: https://doi.org/10.1016/j.aej.2024.05.112
Missing data occurs frequently in sample surveys. Numerous imputation techniques have been proposed to tackle the problem of missing data, and, in fact, limited works are available in the literature to deal with the issue of missingness while the data are ridden with measurement errors (ME). In addition, no work has been done on the robustness to handle the issue of missing data when it is contaminated with correlated measurement errors (CME). This article proposes some robust imputation methods (RIM) to impute the missing data in the presence of CME. The mean square error (MSE) of the proposed RIM is derived from the first-order approximation and examined with the MSE of the conventional imputation methods. The results are theoretically established and explained using a vast simulation study. Two applications of real data sets are presented to illustrate the efficiency and superiority of the suggested estimators relative to some estimators considered in this study.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.aej.2024.05.112
- OA Status
- gold
- Cited By
- 3
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399916084
Raw OpenAlex JSON
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https://openalex.org/W4399916084Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.aej.2024.05.112Digital Object Identifier
- Title
-
On some robust imputation methods in presence of correlated measurement errors with real data applicationsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-06-22Full publication date if available
- Authors
-
Anoop Kumar, Shashi Bhushan, Shivam Shukla, Ibrahim M. Almanjahie, M. J. S. Khan, Amer Ibrahim Al‐OmariList of authors in order
- Landing page
-
https://doi.org/10.1016/j.aej.2024.05.112Publisher landing page
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.aej.2024.05.112Direct OA link when available
- Concepts
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Missing data, Imputation (statistics), Estimator, Robustness (evolution), Computer science, Mean squared error, Data mining, Statistics, Observational error, Econometrics, Mathematics, Biochemistry, Gene, ChemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3Per-year citation counts (last 5 years)
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34Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| publication_date | 2024-06-22 |
| publication_year | 2024 |
| referenced_works | https://openalex.org/W2100358124, https://openalex.org/W6676551524, https://openalex.org/W2048782936, https://openalex.org/W2069224700, https://openalex.org/W2110976713, https://openalex.org/W3002807141, https://openalex.org/W6789944686, https://openalex.org/W4210650911, https://openalex.org/W4379519581, https://openalex.org/W4387096303, https://openalex.org/W3193393179, https://openalex.org/W6856043747, https://openalex.org/W4389228025, https://openalex.org/W6861401665, https://openalex.org/W4362721738, https://openalex.org/W6862285792, https://openalex.org/W2044647200, https://openalex.org/W2021552997, https://openalex.org/W2971974983, https://openalex.org/W4377967117, https://openalex.org/W2344931811, https://openalex.org/W4385399968, https://openalex.org/W4386965346, https://openalex.org/W6857036793, https://openalex.org/W3120346508, https://openalex.org/W6846895001, https://openalex.org/W4385767051, https://openalex.org/W4236315606, https://openalex.org/W4387734470, https://openalex.org/W3126315327, https://openalex.org/W2029685080, https://openalex.org/W4392244928, https://openalex.org/W4308630541, https://openalex.org/W4392366093 |
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| corresponding_author_ids | https://openalex.org/A5010392796 |
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