@article{
 title = {New logarithmic type imputation techniques in presence of measurement errors},
 type = {article},
 year = {2023},
 keywords = {Imputation,Measurement errors,Missing data},
 pages = {707-730},
 volume = {71},
 id = {a70c3eba-84b1-36f2-bb85-4a809d1009b6},
 created = {2023-04-08T10:59:22.904Z},
 file_attached = {false},
 profile_id = {3d6b17c2-7de8-3a82-bf4f-ddb0e3081e5f},
 last_modified = {2023-04-29T15:11:44.255Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 source_type = {JOUR},
 private_publication = {false},
 abstract = {Several imputation techniques have been constructed to sort out the missing data issue. However, there are few papers that address the missing data issue in measurement error (ME). This article proposes some logarithmic type imputation techniques to tackle the missing data issue when the data are commingled with ME. The proffered imputation techniques’ mean square error is calculated to a first order approximation. The relative performance of the suggested imputation techniques against the contemporary imputation techniques is performed. Furthermore, the theoretical results are exemplified using an extensive simulation study based on artificially generated population. The appropriate recommendations have been suggested to the surveyors for real-life problems.},
 bibtype = {article},
 author = {Bhushan, Shashi and Kumar, Anoop and Shukla, Shivam and Bakr, M E and Tashkandy, Yusra A and Hossain, Md. Moyazzem},
 doi = {10.1016/j.aej.2023.03.035},
 journal = {Alexandria Engineering Journal}
}

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