Comparisons of imputation methods on different types of survey research data: A continuous variable
Missing data problems are commonly unavoidable and affect the outcome of many studies. The insufficiency of data resulted in inaccurate results and predictions in many statistical analyses. In survey studies, datasets with missing values require some imputation method to continue with reliable stati...
Published in: | AIP Conference Proceedings |
---|---|
Main Author: | Rahman H.A.A.; Hidayat T.; Rahman A.A.; Razif A.M. |
Format: | Conference paper |
Language: | English |
Published: |
American Institute of Physics
2024
|
Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203152282&doi=10.1063%2f5.0225435&partnerID=40&md5=c3a339b3e483de40ed29a1a0aa79b6f9 |
Similar Items
-
Preliminary study on multiple imputation for nonresponse in survey data with feature selection
by: Jasin A.M.; Asmat A.
Published: (2023) -
Imputation Analysis for Time Series Air Quality (PM10) Data Set: A Comparison of Several Methods
by: Shaadan N.; Rahim N.A.M.
Published: (2019) -
Time Series Data and Recent Imputation Techniques for Missing Data: A Review
by: Zainuddin A.; Hairuddin M.A.; Yassin A.I.M.; Latiff Z.I.A.; Azhar A.
Published: (2022) -
A comparison of model-based imputation methods for handling missing predictor values in a linear regression model: A simulation study
by: Hasan H.; Ahmad S.; Osman B.M.; Sapri S.; Othman N.
Published: (2017) -
Estimation of missing values in air pollution dataset by using various imputation methods
by: Sukatis F.F.; Noor N.M.; Zakaria N.A.; Ul-Saufie A.Z.; Suwardi A.
Published: (2019)