Measuring Performance of Ratio-Exponential-Log Type General Class of Estimators Using Two Auxiliary Variables | Zendy

Javid Shabbir | Zendy; Shakeel Ahmed | Zendy; Aamir Sanaullah | Zendy; Ronald Onyango | Zendy

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Measuring Performance of Ratio-Exponential-Log Type General Class of Estimators Using Two Auxiliary Variables

Author(s) -

Javid Shabbir,

Shakeel Ahmed,

Aamir Sanaullah,

Ronald Onyango

Publication year - 2021

Publication title -

mathematical problems in engineering

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.262

H-Index - 62

eISSN - 1026-7077

pISSN - 1024-123X

DOI - 10.1155/2021/5245621

Subject(s) - estimator , mathematics , mean squared error , statistics , exponential type , extremum estimator , population , type (biology) , exponential function , ratio estimator , class (philosophy) , efficiency , m estimator , efficient estimator , minimum variance unbiased estimator , computer science , mathematical analysis , artificial intelligence , biology , ecology , demography , sociology

In this paper, a ratio-exponential-log type general class of estimators is proposed in estimating the finite population mean using two auxiliary variables when population parameters of the auxiliary variables are known. From the proposed estimator, some special estimators are identified as members of the proposed general class of estimators. The mean square error (MSE) expressions are obtained up to the first order of approximation. This study finds that the proposed general class of estimators outperforms as compared to the conventional mean estimator, usual ratio estimators, exponential-ratio estimators, log-ratio type estimators, and many other competitor regression type estimators. Four real-life applications are used for efficiency comparison.

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