A Generalized Exponential Type Estimator of Population Mean in the Presence of Non-Response

Muneer, Siraj; Shabbir, Javid; Khalil, Alamgir

doi:10.21307/stattrans-2018-015

Cited by 6 publications

(7 citation statements)

References 15 publications

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“…Sharma and Pal (2018) proposed improved ratio type estimator for the estimation of population mean in presence of random non-response. Muneer et al (2018) suggested a generalized exponential ratio type estimator of population mean in the presence of non-response Toutenberg and Srivastava (1998); Toutenberg and Srivastava (2003) studied the problem of estimation of ratio of two population means and the population mean respectively in survey sampling when some observations are missing on study and auxiliary variables. Sinha (2007, 2009) also suggested enhanced estimation of the ratio of the two population means using multi auxiliary characteristics in the presence of non-response.…”

Section: Introductionmentioning

confidence: 99%

Dexterous Estimation of Population Mean in Survey Sampling Under Non-Response Error

Yadav

2019

Int J Math, Eng, Manag Sci

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In this scripture, we ponder the problem of efficient estimation of population mean of study variable utilizing information on highly correlated auxiliary variables under the presence of non-response on either of the variables. For this purpose, we suggest, an improved estimator under three different situations of non-response. Under the first situation, estimation of population mean is done with the problem of non-response on both the study and the auxiliary variables with the additional condition that the population means of the auxiliary variables are known. The second situation is to estimate the population mean of primary variable when the problem of non-response is only on the primary variable but the population means of the auxiliary variables are known while under the third situation estimation is performed with the problem of non-response on both the study and the auxiliary variables but population mean of one of the auxiliary variables is unknown. We study the sampling properties of the suggested estimator under above three different situations of non-response. We compare the proposed estimator with the competing estimators of population mean, under three different situations of non-response. The efficiency conditions are obtained for all three situations. A numerical study is also carried out to verify the efficiency conditions.

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Section: Introductionmentioning

confidence: 99%

Dexterous Estimation of Population Mean in Survey Sampling Under Non-Response Error

Yadav

2019

Int J Math, Eng, Manag Sci

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“…T ) under call-back has minimum MSEs and higher PREs than most of the estimators under study with the exception of Salim and Onyango [29] so t , Muneer et al [28] estimators  …”

Section:    mentioning

confidence: 87%

“…Munneer et al [28] proposed exponential type estimators of population mean in the presence of non-response as in ( 5) -( 14)…”

Section: mentioning

confidence: 99%

Enhanced Robust Estimators for Estimating Population Means When Confronted with Non-Response and Measurement Error

Audu,

Usman,

Mohammad

et al. 2023

AJPAS

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The use of estimators in statistics, quality assurance, and survey methodology can never be over flogged just as the use of sampling. Two of the major challenges of statisticians or surveyors due encounter at the course of data collection in the field of medical and social sciences is non-response and measurement errors. This poses serious problem during data compilation, computation and estimation stages. In this paper, a robust-based classes of estimators are proposed in the presence of non-response and measurement errors through the use of imputation scheme incorporated with measurement errors parameters. The properties of the proposed estimators (Biases & MSES) were derived up to the second degree approximation using Taylors’s series approach. The conditions for the efficiencies of the proposed estimators over the existing estimators was also considered and established in this research. The empirical study conducted using simulated data from normal distribution, exponential distribution, chi-square distribution, uniform distribution, gamma distribution and poison distribution revealed that the modified classes of estimators of the proposed imputation schemes are more efficient and satisfactory than the compared existing estimators. Thus, the proposed modified classes of estimators under imputation scheme were recommended for use in the real life situation especially in the presence of non-response and measurement errors during data analysis and estimation stages.

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“…In the context of nonrespond, [11] introduced an elevated ratio estimator for estimating Ȳ. For the issue of non-respond, [12] proposed an exponential type generalized estimator of Ȳ. While some observations on Y and X are missing, [13,14] investigated the challenge of estimating the ratio of two Ȳ in survey sampling.…”

Section: Introductionmentioning

confidence: 99%

Restructured searls family of estimators of population mean in the presence of nonresponse

Ahmadini

Yadav²,

Yadav³

et al. 2022

Front. Appl. Math. Stat.

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Nonresponse is a major problem encountered by surveyors when conducting sampling surveys. The present study suggested a naïve modified Searls method for the elevated estimation of the population mean of the primary variable under investigation by utilizing the known auxiliary parameters. The bias along with the mean squared error (MSE) of the introduced estimator is calculated up to the approximation of the first order. We compared the presented estimator with a competing usual unbiased estimator and other competing population mean estimators regarding the issue of nonresponse. The efficiency criteria of the introduced estimator outperforming the other estimators in the competition are determined and verified using five real data sets. The MSEs for the introduced estimator and the other estimators in the competition are calculated for the five considered populations. The estimator with the least MSE or highest percentage relative efficiency (PRE) is recommended for practical exercise in different areas of applications.

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A Generalized Exponential Type Estimator of Population Mean in the Presence of Non-Response

Cited by 6 publications

References 15 publications

Dexterous Estimation of Population Mean in Survey Sampling Under Non-Response Error

Dexterous Estimation of Population Mean in Survey Sampling Under Non-Response Error

Enhanced Robust Estimators for Estimating Population Means When Confronted with Non-Response and Measurement Error

Restructured searls family of estimators of population mean in the presence of nonresponse

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