An Efficient Ratio-Cum-Exponential Estimator for Estimating the Population Distribution Function in the Existence of Non-Response Using an SRS Design

Ayesha Khalid; Aamir Sanaullah; Mohammed M.A. Almazah; Fuad S. Al-Duais

doi:10.3390/math11061312

An Efficient Ratio-Cum-Exponential Estimator for Estimating the Population Distribution Function in the Existence of Non-Response Using an SRS Design

Ayesha Khalid, Aamir Sanaullah, Mohammed M.A. Almazah, Fuad S. Al-Duais

Mathematics

Research output: Contribution to journal › Article › peer-review

3 Scopus citations

Abstract

To gain insight into various phenomena of interest, cumulative distribution functions(CDFs) can be used to analyze survey data. The purpose of this study was to present an efficient ratiocum-exponential estimator for estimating a population CDF using auxiliary information under twoscenarios of non-response. Up to first-order approximation, expressions for the bias and mean squared error (MSE) were derived. The proposed estimator was compared theoretically and empirically, with the modified estimators. The proposed estimator was found to be better than the modified estimators based on present-relative efficiency PRE and MSE criteria under the specific conditions.

Original language	English
Article number	1312
Journal	Mathematics
Volume	11
Issue number	6
DOIs	https://doi.org/10.3390/math11061312
State	Published - Mar 2023

Keywords

auxiliary information
cumulative distribution function
exponential estimator
non-response
sub-sampling of non-respondents

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title = "An Efficient Ratio-Cum-Exponential Estimator for Estimating the Population Distribution Function in the Existence of Non-Response Using an SRS Design",

abstract = "To gain insight into various phenomena of interest, cumulative distribution functions(CDFs) can be used to analyze survey data. The purpose of this study was to present an efficient ratiocum-exponential estimator for estimating a population CDF using auxiliary information under twoscenarios of non-response. Up to first-order approximation, expressions for the bias and mean squared error (MSE) were derived. The proposed estimator was compared theoretically and empirically, with the modified estimators. The proposed estimator was found to be better than the modified estimators based on present-relative efficiency PRE and MSE criteria under the specific conditions.",

keywords = "auxiliary information, cumulative distribution function, exponential estimator, non-response, sub-sampling of non-respondents",

author = "Ayesha Khalid and Aamir Sanaullah and Almazah, \{Mohammed M.A.\} and Al-Duais, \{Fuad S.\}",

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AU - Khalid, Ayesha

AU - Sanaullah, Aamir

AU - Almazah, Mohammed M.A.

AU - Al-Duais, Fuad S.

PY - 2023/3

Y1 - 2023/3

N2 - To gain insight into various phenomena of interest, cumulative distribution functions(CDFs) can be used to analyze survey data. The purpose of this study was to present an efficient ratiocum-exponential estimator for estimating a population CDF using auxiliary information under twoscenarios of non-response. Up to first-order approximation, expressions for the bias and mean squared error (MSE) were derived. The proposed estimator was compared theoretically and empirically, with the modified estimators. The proposed estimator was found to be better than the modified estimators based on present-relative efficiency PRE and MSE criteria under the specific conditions.

AB - To gain insight into various phenomena of interest, cumulative distribution functions(CDFs) can be used to analyze survey data. The purpose of this study was to present an efficient ratiocum-exponential estimator for estimating a population CDF using auxiliary information under twoscenarios of non-response. Up to first-order approximation, expressions for the bias and mean squared error (MSE) were derived. The proposed estimator was compared theoretically and empirically, with the modified estimators. The proposed estimator was found to be better than the modified estimators based on present-relative efficiency PRE and MSE criteria under the specific conditions.

KW - auxiliary information

KW - cumulative distribution function

KW - exponential estimator

KW - non-response

KW - sub-sampling of non-respondents

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JO - Mathematics

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ER -

An Efficient Ratio-Cum-Exponential Estimator for Estimating the Population Distribution Function in the Existence of Non-Response Using an SRS Design

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Keywords

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