Improved Ratio Estimators for Estimating Population Mean Using Auxiliary Information

Muili, Jamiu Olasunkanmi; Adebiyi, Ayodele Ariyo

doi:10.29322/ijsrp.10.05.2020.p101111

Cited by 5 publications

(3 citation statements)

References 2 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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“…Subramani and Kumarapandiyan [11,12] have also suggested modified ratio estimators using the known median and the coefficient of kurtosis, median and coefficient of skewness, median and the coefficient of variation, and median and the coefficient of correlation. Other authors that had work in this direction include Hartley-Ross (1954), Quenouille's [13], Singh [14,15], Naik and Gupta [16], Kadilar and Cingi [17], Singh and Espejo [7][8][9], Shabbir and Yaab [18], Abu-Dayeh et al [19], Kadilar and Cingi [20], Jhajj et al [21], Khoshnevisan et al [22], Kadilar and Cingi [23], Perri [24], Singh et al [25], Gupta and Shabbir [26], Sharma and Taylor [27], Grover and Kaur [28], Subramani and Kumarapandiyan [11,12,29,30], Singh and Kumar [31], Nadia and Mohammad [32], Audu and Singh [33], Tailor et al [34], Lu [35], Sharma and Singh [36], Lu and Yan [37], Verma et al [38], Audu and Adewara [39,40], Muili and Audu [41], Muili et al [42,43,44 Zakari et al [61,62] Regression estimator is used to estimate the population characteristics such as population mean, total and variance when the regression lin...…”

Section: Introductionmentioning

confidence: 99%

On the Efficiency of Modified Regression-type Estimators Using Robust Regression Slopes and Non-conventional Measures of Dispersion

Audu

Gidado

Dauran

et al. 2022

ARJOM

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Supplementary variables associated with the study variables have been identified to be helpful in improving the efficiency of ratio, product and regression estimators both at planning and estimation stages. The existing regression-based estimators are functions of regression slopes and known auxiliary variables which are sensitive to outliers. Zaman & Bulut [1] and Zaman [2] addressed the issue of regression slopes in the aforementioned estimators using robust regression slopes like Huber-M, Hampel-M, Least Trimmed Squares (LTS) and Least Absolute Deviation (LAD). However, their estimators still utilized known auxiliary functions which are also sensitive to outliers or extreme values. Similarly, Yadav and Zaman [3] suggested non-conventional robust parameters of auxiliary variable which are robust against outliers. However, the problems of effects of outliers on regression slopes were not considered. In this study, the estimators of Zaman & Bulut [1] and Zaman [2] estimators were modified using robust non-conventional measures and Yadav and Zaman [3] estimators were modified using robust regression slopes. The properties (Biases and MSEs) of the modified estimators were derived up to the first order of approximation using Taylor series approach. The efficiency conditions of the proposed estimators over the existing estimators considered in the study were established. The empirical studies were conducted using stimulation data to investigate the efficiency of the proposed estimators over the efficiency of the existing estimators. The results revealed that the proposed estimators have minimum MSEs and higher PREs among all the competing estimators for the simulated populations in the study. This implies that the proposed estimators are more efficient and can produce better estimate of the population mean compared to other existing estimators considered in the study.

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“…Other authors that had worked in this direction include Hartley and Ross (1954), Quenouille (1956), Singh (1965Singh ( , 1967, Naik and Gupta (1991), Kadilar and Cingi (2003), Singh and Espejo (2003), Shabbir and Yaab (2003), Abu-Dayeh et al (2003), Kadilar and Cingi (2005), Jhajj et al (2006), Khoshnevisan et al (2007), Perri (2007), , Gupta and Shabbir (2008), Sharma and Tailor (2010), Subramani and Kumarapandiyan (2012), Subramani and Kumarapandiyan (2013), Subramani and Kumarapandiyan (2014), Singh and Kumar (2011), Tailor et al (2012), Lu (2013), Sharma and Singh (2014), Lu and Yan (2014), Verma et al (2015), Ahmed and Singh (2015), Audu and Adewara (2017a,b), Audu and Muili (2019), Muili et al (2020), Singh et al (2020), Audu et al (2020a,b,c,d), Audu et al (2021a,b,c,d,e,f), Ahmed et al (2016a, Yunusa et al (2021), Zaman et al (2021), Audu and Singh (2021).…”

mentioning

confidence: 99%

Modified robust regression-type estimators with multi-auxiliary variables using non-conventional measures of dispersion

Audu,

Gidado,

Dauran

et al. 2023

Nig J Bas App Sci

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Auxiliary variables correlated with the study variables have been identified to be useful in improving the efficiency of ratio, product and regression estimators both at planning and estimation stages. The existing regression-based estimators are functions of auxiliary variables which are sensitive to outliers. In this paper, a modified class of estimators is proposed using robust non-conventional measures of dispersion which are robust against outliers or extreme values. The properties (Biases and Mean Squared Errors (MSEs)) of the modified class of estimator were derived up to the first order of approximation using Taylor series approach. The empirical studies were conducted using stimulation to investigate the efficiency of the proposed estimators over the efficiency of the existing estimators. The results revealed that the proposed estimators have minimum MSEs and higher Percentage Relative Efficiencies (PREs) among all the competing estimators. These results implied that the proposed estimators are more efficient and can produce better estimate of the population mean compared to other existing estimators considered in the study. Therefore, it can be concluded that proposed estimators have better predictive power for estimating population mean when the study (interest) variables are characterized with outliers or extreme values.

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Improved Ratio Estimators for Estimating Population Mean Using Auxiliary Information

Cited by 5 publications

References 2 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

On the Efficiency of Modified Regression-type Estimators Using Robust Regression Slopes and Non-conventional Measures of Dispersion

Modified robust regression-type estimators with multi-auxiliary variables using non-conventional measures of dispersion

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