Robust Regression-Ratio-Type Estimators of the Mean Utilizing Two Auxiliary Variables: A Simulation Study

Zaman, Tolga; Dünder, Emre; Audu, Ahmed; Alilah, David Anekeya; Shahzad, Usman; Hanif, Muhammad

doi:10.1155/2021/6383927

Cited by 25 publications

(25 citation statements)

References 17 publications

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“…The first attempt was made by Cochran [1] to investigate the problem of estimation of the population mean when auxiliary variables are present and he proposed the usual ratio estimator of population mean. Other authors like, Hartley-Ross [2], Quenouille's [3], Singh [4,5], Abu-Dayeh et al [6], Kadilar and Cingi [7], Khoshnevisan et al [8], Perri [9], Singh et al [10], Singh et al [11], Singh and Kumar [12], Tailor et al [13], Lu [14], Sharma and Singh [15], Lu and Yan [16], Verma et al [17], Audu and Singh [18], Hafeez and Shabbir [19], Audu et al [20,21], Audu et al [18,[22][23][24][25][26], Audu and Adewara [27], Ahmed et al [28][29][30][31], Hafeez et al [32], Yunusa et al [33], Zaman et al [34], Hafeez et al [35], Ahmed and Singh [36], Neyman [37], Singh et al [38] have also considered the problem of estimating the mean and other parameters of study variables when auxiliary variables are made available. There are many practical situations when auxiliary information is qualitative in nature, i.e, auxiliary information is available in the form of an attribute, such as the height of a person may depend on the fact that whether the person is male or female, the efficiency of a dog may depend on the particular breed of that dog, or the yield of whea...…”

Section: Introductionmentioning

confidence: 99%

Modified Classes of Regression-Type Estimators of Population Mean in the Presences of Auxiliary Attribute

Audu

Abdulazeez

Danbaba

et al. 2022

ARJOM

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The use of relevant information from auxiliary variable at the estimation stage and design stage to obtain reliable and efficient estimate is a common practice is a sample survey. But situations arise when the available auxiliary information are attribute in nature. There are some existing estimators based on auxiliary attribute in literature, however, they are less efficient when the bi-serial correlation between the study variable and auxiliary attribute is negative. Also, some depend on an unknown parameter of the study variable (Cy) which makes their applicability of the estimators in real life situations not possible unless if the value is estimated using a large sample which requires additional resources. In this work, the concept of regression base estimator was used to obtain estimators that are independent of unknown population parameter of the study variable and applicable for both negative and positive correlations. 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 estimation over the existing estimator considered in the study were established. The empirical studies were conducted using both existing population parameters and 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 PREs among all the competing estimators. These imply 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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Section: Introductionmentioning

confidence: 99%

Modified Classes of Regression-Type Estimators of Population Mean in the Presences of Auxiliary Attribute

Audu

Abdulazeez

Danbaba

et al. 2022

ARJOM

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“…Zaman and Bulut [16] provided the ratio estimators for population variance considering MCD and MVE robust covariance estimates, both simple and stratified random sampling. Zaman et al [17] presented the robust regression-ratio-type estimators of the mean utilizing two auxiliary variables. Grover and Kaur [18] developed the regression-type estimators of population mean with two auxiliary variables using the robust regression technique.…”

Section: Introductionmentioning

confidence: 99%

A new class of robust ratio estimators for finite population variance

Zaman

Bulut

2022

Scientia Iranica

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It is a general practice to use robust estimates to improve ratio estimators using functions of the parameters of an auxiliary variable. In this study, a new class of robust estimators based upon the minimum covariance determinant (MCD) and the minimum volume ellipsoid (MVE) robust covariance estimates have been suggested for estimating population variance in the presence of outlier values in the data set for the simple random sampling. The expression for the mean square error (MSE) of the proposed class of estimators is derived from the first degree of approximation. The efficiency of the proposed class of robust estimators is compared with some competing estimators discussed in the literature, and found that proposed estimators are better than other mentioned estimators here. In addition, real data set and simulation studies are performed to present the efficiencies of the estimators. We demonstrate theoretically and numerically that the proposed class of estimators performs better than all other competitor estimators under all situations.

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“…These moments provide outrageous outcomes in the presence of extreme (or outliers) values. For settling the issue of extreme values, a lot of research has been carried out, for mean estimation using ratio, regression, and robust regression techniques (Zaman and Bulut, 2,3 and Zaman et al 21 ), for variance estimation using ratio and regression strategies (Abid et al 22 ), for variance estimation using calibration technique (Shahzad et al 12,13 ). However, none of the work was carried out for the estimation of CV in presence of extreme events, which is one of the major RMD.…”

Section: Introductionmentioning

confidence: 99%

“…where 21 ), for variance estimation using ratio and regression strategies (Abid et al 22 ), for variance estimation using calibration technique (Shahzad et al 12,13 ). However, none of the work was carried out for the estimation of CV in presence of extreme events, which is one of the major RMD.…”

Section: Introductionmentioning

confidence: 99%

Estimation of coefficient of variation using linear moments and calibration approach for nonsensitive and sensitive variables

Shahzad

Ahmad

Hanif

et al. 2022

Concurrency and Computation

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Assessment of coefficient of variation (CV) is of major importance in numerous examinations. However, the appearance of extreme observations raises concerns about the outcomes of CV estimates based on conventional moments. So, motivated by some recent developments in finite sampling theory, we propose some new estimators of CV based on the properties of linear moments (L-moments and Trimmed L-moments), which are highly robust whenever outliers or extreme observations appear in a dataset.The proposed estimators are initially established on the premise that the variable of interest is nonsensitive which deals with the subjects that do not embarrass respondents when asked about them explicitly. These estimators are also applied to situations where the variable of interest is associated with sensitive issues that cause measurement errors resulting from nonresponse or unreliable reporting where such issues can be mitigated by increasing respondent participation by scrambled response models which obscure the true value of the sensitive variable. Four models are considered for this article: additive, multiplicative, mixed, and combined additive-multiplicative models. Finally, in both nonsensitive and sensitive settings, real-life data sets are employed to undertake simulation-based analysis. In all cases, the proposed estimators considerably increased efficiency.

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Robust Regression-Ratio-Type Estimators of the Mean Utilizing Two Auxiliary Variables: A Simulation Study

Cited by 25 publications

References 17 publications

Modified Classes of Regression-Type Estimators of Population Mean in the Presences of Auxiliary Attribute

Modified Classes of Regression-Type Estimators of Population Mean in the Presences of Auxiliary Attribute

A new class of robust ratio estimators for finite population variance

Estimation of coefficient of variation using linear moments and calibration approach for nonsensitive and sensitive variables

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