Compromised-Imputation and EWMA-Based Memory-Type Mean Estimators Using Quantile Regression
Survey sampling commonly faces the challenge of missing information, prompting the development of various imputation-based mean estimation methods to address this concern. Among these, ratio-type regression estimators have been devised to compute population parameters using only current sample data....
Main Authors: | Mohammed Ahmed Alomair, Usman Shahzad |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-10-01
|
Series: | Symmetry |
Subjects: | |
Online Access: | https://www.mdpi.com/2073-8994/15/10/1888 |
Similar Items
-
A chain regression exponential type imputation method for mean estimation in the presence of missing data
by: Kanisa Chodjuntug, et al.
Published: (2022-08-01) -
Quantile Regression-Based Multiple Imputation of Missing Values — An Evaluation and Application to Corporal Punishment Data
by: Kristian Kleinke, et al.
Published: (2021-09-01) -
Efficient Difference and Ratio-Type Imputation Methods under Ranked Set Sampling
by: Shashi Bhushan, et al.
Published: (2023-06-01) -
A New Class of Quantile Regression Ratio-Type Estimators for Finite Population Mean in Stratified Random Sampling
by: Tuba Koç, et al.
Published: (2023-07-01) -
A new imputation method for population mean in the presence of missing data based on a transformed variable with applications to air pollution data in Chiang Mai, Thailand
by: Natthapat Thongsak, et al.
Published: (2023-09-01)