Ratio Estimators for Estimating Population Mean in Simple Random Sampling Using Auxiliary Information

Subzar, Mir; Maqbool, Showkat; Raja, T. A.; Abid, Muhammad

doi:10.18576/amisl/060304

Cited by 9 publications

(7 citation statements)

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“…To illustrate numerically, two population data sets have been considered one with high and one with low correlation coefficient. The result obtained in table 2, revealed that the proposed estimators, 1 T , 2 T , Institute of Science, BHU Varanasi, India Subramani and Kumarapandiyan (2012), Abid et al (2016), Subzar et al (2017), Subzar et al (2018a), Subzar et al (2018b), Subzar et al (2018c), and Yadav and Zaman (2021). This indicates that the proposed estimators are more efficient and can produce better estimate of population mean than that of existing estimators including Yadav and Zaman (2021).…”

Section: Discussionmentioning

confidence: 87%

“…Difference-cum-ratio-type estimators were suggested by Kadilar and Cingi (2004) by using coefficient of variation and ____________________________ * Corresponding Author kurtosis of auxiliary variable X and modified by Kadilar and Cingi (2006) by adding correlation coefficient. Later on, extended subsequently by Subramani and Kumarapandiyan (2012), Abid et al (2016), Subzar et al (2017), Subzar et al (2018a), Subzar et al (2018b), Subzar et al (2018c) by using known parameters of auxiliary variable . Recently, Yadav and Zaman (2021) suggested some class of estimators by using conventional and nonconventional location parameters of auxiliary variable and sample size and found that their estimators were more efficient than the estimators developed by above mentioned authors.…”

Section: Introductionmentioning

confidence: 99%

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Modified Classes of Regression Type Exponential Estimators of Population Mean

Singh¹,

Saleh²,

Audu³

2021

JSR

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In this paper, classes of modified regression--type exponential estimators for estimating population means using known parameters of auxiliary variable have been suggested. The biases and mean square errors of the suggested classes of estimators have been derived up to first order of approximation and theoretical conditions for which the suggested estimators are more efficient than other existing estimators considered in the study have also been established. Numerical illustration has been conducted and the results revealed that the suggested estimators are more efficient than existing estimators even for extreme cases of correlation coefficient.

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Section: Discussionmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Modified Classes of Regression Type Exponential Estimators of Population Mean

Singh¹,

Saleh²,

Audu³

2021

JSR

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“…In this article, considering the theoretical comparisons in Section 4, and the findings of the numerical application and simulation, it is apparent that suggested estimators using some robust regression methods perform better than the competing estimators given by Kadilar and Cingi, 21 Subramani and Kumarapandiyan, 22,23 Abid et al 24 and Subzar et al 25 when there are outliers in data. The breakpoint of the LAD and M-estimations is 1∕n.…”

Section: Discussionmentioning

confidence: 94%

“…When condition (46) is satisfied, the suggested estimators given in (36)-(41) perform better than Subzar et al 25 estimators.…”

Section: Efficiency Comparisonsmentioning

confidence: 91%

Robust ratio‐type estimators for finite population mean in simple random sampling: A simulation study

Zaman

Bulut

Yadav

2022

Concurrency and Computation

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In this study, ratio estimators are proposed by utilizing some robust techniques to get the maximum benefit of the auxiliary variable for the estimation of the population mean in simple random sampling. The expressions for mean squared error are derived for the first degree of approximation. Theoretical comparisons demonstrate that the suggested estimators having robust regression estimates perform better than the existing estimators under certain conditions. Theoretical findings are supported with the aid of the original dataset in an application. In addition, a simulation study is also conducted to evaluate the performance of the suggested estimators.

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“…Ozgul & Cingi [66] proposed a new class of exponential regression cum ratio estimator using functions of any known population parameters of the auxiliary variable, such as standard deviation, coefficient of variation, coefficient of skewness, coefficient of kurtosis and coefficient of correlation of the auxiliary variable for the estimation of finite population mean. Several authors like Kadilar and Cingi [67], Kadilar and Cingi [68], Subramani and Kumarapandiyan (2006), Abid et al [69], Subzar et al [70], Subzar et al [71], Subzar et al [72], Subzar et al [73] have proposed some regression-based estimators which utilized known functions of auxiliary variables. However, these auxiliary parameters are sensitive to outliers or extreme values that do present in the population distributions.…”

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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Ratio Estimators for Estimating Population Mean in Simple Random Sampling Using Auxiliary Information

Cited by 9 publications

References 0 publications

Modified Classes of Regression Type Exponential Estimators of Population Mean

Modified Classes of Regression Type Exponential Estimators of Population Mean

Robust ratio‐type estimators for finite population mean in simple random sampling: A simulation study

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

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