Ratio exponential type estimators with imputation for missing data in sample surveys

Prasad, Shakti

doi:10.3233/mas-170386

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…Following the Prasad (2016Prasad ( & 2017, a product exponential method of imputation and their corresponding estimator has suggested for estimating the population meanȲ in sample surveys. The suggested imputation method, After imputation, the data take the form:…”

Section: Suggested Methods and Their Estimatormentioning

confidence: 99%

Product Exponential Method of Imputation in Sample Surveys

Prasad

2018

Statistics in Transition New Series

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In this paper, a product exponential method of imputation has been suggested and their corresponding resultant point estimator has been proposed for estimating the population mean in sample surveys. The expression of bias and the mean square error of the suggested estimator has also been derived, up to the first order of large sample approximations. Compared with the mean imputation method, Singh and Deo (Statistical Papers (2003)) and Adapted estimator (Bahl and Tuteja (1991)), the simulation studies show that the suggested estimator is the most efficient estimator.

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Section: Suggested Methods and Their Estimatormentioning

confidence: 99%

Product Exponential Method of Imputation in Sample Surveys

Prasad

2018

Statistics in Transition New Series

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“…Using MCAR response mechanism, Ahmed et al (2006), Singh & Horn (2000), Toutenburg et al (2008), Singh (2009), Singh et al (2010), Singh et al (2016), Gira (2015), Kadilar & Cingi (2008), Diana & Francesco Perri (2010), Prasad (2017), Prasad (2018a), Prasad (2018b), Prasad (2019), Prasad (2021) have proposed numerous new imputation techniques in sampling theory.…”

Section: Introductionmentioning

confidence: 99%

Imputation of Missing Data Through Product Type Exponential Methods in Sampling Theory

Prasad,

Yadav

2023

Rev. colomb. estad.

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Some efficient product type exponential imputation methods are proposed in this article to tackle the problem of incomplete values in sampling theory. To investigate the effectiveness of proposed exponential methods, the behaviours of the considered estimators are compared in two scenarios: with and without nonresponse. The simulation studies show that the proposed resultant estimators outperform other existing estimators in this literature.

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“…Bhushan and Pandey [7,8] defined an optimal imputation strategy for mean estimation. Prasad [9] developed exponentialtype estimators for missing observations. Taking motivation from Zaman Bulut [10,11], Shahzad et al [12] constructed imputation-based mean estimators using robust covariance matrices.…”

Section: Introductionmentioning

confidence: 99%

Compromised-Imputation and EWMA-Based Memory-Type Mean Estimators Using Quantile Regression

Alomair,

Shahzad

2023

Symmetry

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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. However, recent pioneering research has revolutionized this approach by integrating both past and current sample information through the application of exponentially weighted moving averages (EWMA). This groundbreaking methodology has given rise to the creation of memory-type estimators tailored for surveys conducted over time. In this paper, we present novel imputation-based memory-type mean estimators that leverage EWMA and quantile regression to handle missing observations. For the performance assessment between traditional, adapted and proposed estimators, real-life time-scaled datasets related to the stock market and humidity are considered. Furthermore, we conduct a simulation study using an asymmetric dataset to further validate the effectiveness of the introduced estimators. The humidity data results show that the proposed estimators (Tpq(0.25), Tpq(0.45), Tpq(0.25)′, Tpq(0.45)′, Tpq(0.25)″, Tpq(0.45)″) have the minimum MSE. The stock market data results show that the proposed estimators (Tpq(0.85), Tpq(0.85)′, Tpq(0.85)″) also have the minimum MSE. Additionally, the simulation results demonstrate that the proposed estimators (Tpq(0.45), Tpq(0.45)′, Tpq(0.45)″) have the minimum MSE when compared to traditional and adapted estimators. Therefore, in conclusion, the use of the proposed estimators is recommended over traditional and adapted ones.

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Ratio exponential type estimators with imputation for missing data in sample surveys

Cited by 6 publications

References 8 publications

Product Exponential Method of Imputation in Sample Surveys

Product Exponential Method of Imputation in Sample Surveys

Imputation of Missing Data Through Product Type Exponential Methods in Sampling Theory

Compromised-Imputation and EWMA-Based Memory-Type Mean Estimators Using Quantile Regression

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