Regression-Cum-Exponential Ratio Imputation Class of Estimators of Population Mean in the Presence of Non-Response

Audu, Ahmed; Olawoyin, Ishaq Olatunji; Zakari, Yahaya; Wisdom, Daniel Dauda; Muili, Jamiu Olasunkanmi; Ndatsu, A. M.

doi:10.5455/sf.71109

Cited by 8 publications

(8 citation statements)

References 7 publications

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“…Abid et al [15] proposed a class of ratio estimators incorporated non-conventional location parameters for the estimation of population mean. Other researchers are Kadilar and Cingi [16], Koyuncu and Kadilar [17], Yan and Tian [18], Subramani and Kumarapandiyan [19], Yadav et al [20], Gupta and Yadav [21], Muili et al [22][23][24][25] Audu et al [26], [27] Muili et al [28], etc.…”

Section: Methodsmentioning

confidence: 99%

Modified Ratio-Cum-Product Estimators of Population Mean Using Two Auxiliary Variables

Muili

Erinola

Yunusa

et al. 2020

AJRCoS

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A percentile is one of the measures of location used by statisticians showing the value below which a given percentage of observations in a group of observations fall. A family of ratio-cum-product estimators for estimating the finite population mean of the study variable when the finite population mean of two auxiliary variables are known in simple random sampling without replacement (SRSWOR) have been proposed. The main purpose of this study is to develop new ratio-cum-product estimators in order to improve the precision of estimation of population mean in sample random sampling without replacement using information of percentiles with two auxiliary variables. The expressions of the bias and mean square error (MSE) of the proposed estimators were derived by Taylor series method up to first degree of approximation. The efficiency conditions under which the proposed ratio-cum-product estimators are better than sample man, ratio estimator, product estimator and other estimators considered in this study have been established. The numerical and empirical results show that the proposed estimators are more efficient than the sample mean, ratio estimator, product estimator and other existing estimators.

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Section: Methodsmentioning

confidence: 99%

Modified Ratio-Cum-Product Estimators of Population Mean Using Two Auxiliary Variables

Muili

Erinola

Yunusa

et al. 2020

AJRCoS

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“…In survey sampling, the use of auxiliary characters is frequently recognized to increase the accuracy or precision of the population characteristics estimation procedures. Many researchers have considered the use of auxiliary characters in different forms to construct efficient estimators for population parameters (See Sisodia and Dwivedi [1], Murthy [2], Yadav and Kadilar [3], Singh and Tailor [4], Bahl and Tuteja [5], Singh et al [6], Singh et al [7], Kadilar and Cingi [8], Singh and Solanki [9], Sahai and Ray [10], Srivastava and Jhajj [11], Ahmed et al [12], Audu and Adewara [13], Audu et al [14], Muili et al [15], Khoshnevisan et al [16], Singh and Audu [17], Ahmed et al [18] and Audu and Singh [19], Das and Tripathi [20], Das and Tripathi [21], Patel and Rina [22], Rajyaguru and Gupta [23], Rajyaguru and Gupta [24], Archana and Rao [25], Singh et al [26], Audu et al [27][28][29][30][31][32], Singh et al [33], Muili et al [34], Ishaq et al [35], Audu et al [36], Zakari et al [37]. The issue of estimating variance of populations' characters is of great interest and as received attentions of researchers due to difficulties in controlling variabilities in application.…”

Section: Introductionmentioning

confidence: 99%

Generalized Estimators for Finite Population Variance Using Measurable and Affordable Auxiliary Character

Muhammad

Zakari

Audu

2022

ARJOM

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In this paper, a generalized exponential-type estimator for estimating the population variance using measurable and affordable auxiliary character in single phase sampling has been proposed. Some special cases of the proposed generalized estimator with a = 1 and a = 2 have also been discussed. The expressions for the mean square error and bias of the proposed generalized estimator have been derived. The efficiency of the proposed generalized estimator has been compared theoretically with the existing estimators and the conditions under which the proposed estimators are better than some existing estimators have also been given. Numerical examples with five real data sets to compute MSEs and PREs were conducted to ascertain the efficiency of the proposed estimators over others and the results showed that both of the proposed estimators were more efficient than the estimators considered in literature. Thus, the estimator with perform better than the estimator with a = 1.

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“…However, when the study variables are characterized by non-response, the aforementioned estimators are not applicable. Authors like Singh and Horn [10], Singh and Deo [11], Wang and Wang [12], Toutenburg et al [13], Kadilar and Cingi [14], Singh [15], Diana and Perri [16], Al-Omari et al [17], Singh et al [18], Gira (2015), Singh et al [19], Bhushan and Pandey [20], Prasad [21], Audu et al [22][23][24], have studied different schemes and estimators in the presence of non-response. Situations arise when the auxiliary characters are qualitative in nature e.g.…”

Section: Introductionmentioning

confidence: 99%

Regression-type Imputation Class of Estimators using Auxiliary Attributes

Audu

Ishaq

Abubakar

et al. 2021

ARJOM

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Several imputation schemes and estimators have been proposed by different authors in sample survey. However, these estimators utilized quantitative information of auxiliary characters. In this study, some imputation methods were studied using qualitative information of auxiliary characters and two new imputation schemes using auxiliary attribute have been suggested. The mean squared errors of the proposed estimators were derived up to first order approximation using Taylor series approach. Numerical illustrations with two populations were conducted and the results revealed that the proposed estimator is more efficient.

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Regression-Cum-Exponential Ratio Imputation Class of Estimators of Population Mean in the Presence of Non-Response

Cited by 8 publications

References 7 publications

Modified Ratio-Cum-Product Estimators of Population Mean Using Two Auxiliary Variables

Modified Ratio-Cum-Product Estimators of Population Mean Using Two Auxiliary Variables

Generalized Estimators for Finite Population Variance Using Measurable and Affordable Auxiliary Character

Regression-type Imputation Class of Estimators using Auxiliary Attributes

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