Logarithmic type predictive estimators under simple random sampling

Shashi Bhushan; Anoop Kumar; Md Tanwir Akhtar; Showkat Ahmad Lone; Shashi Bhushan; Anoop Kumar; Md Tanwir Akhtar; Showkat Ahmad Lone

doi:10.3934/math.2022668

AIMS Mathematics

2022, Volume 7, Issue 7: 11992-12010. doi: 10.3934/math.2022668

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Research article

Logarithmic type predictive estimators under simple random sampling

1.
University of Lucknow, Lucknow, U.P., India, 226007
2.
Department of Mathematics & Statistics, Dr. Shakuntala Misra National Rehabilitation University, Lucknow, U.P., India, 226017
3.
Department of Public Health, College of Health Sciences, Saudi Electronic University, KSA
4.
Department of Basic Sciences, College of Science and Theoretical Studies, Saudi Electronic University, 11673, KSA

Received: 01 February 2022 Revised: 26 March 2022 Accepted: 13 April 2022 Published: 21 April 2022
MSC : 62D05

This study introduces a novel predictive estimation approach of the population mean based on logarithmic type estimators as predictor under simple random sampling. The bias and mean square error of the proffered predictive estimators are examined to the approximation of order one. The efficiency conditions are obtained and the performance of the proffered predictive estimators is examined regarding the contemporary predictive estimators existing till date. Further, a broad computational study is also administered utilizing few real and artificially rendered symmetric and asymmetric populations to exemplify the theoretical results.
- bias,
- efficiency,
- mean square error,
- predictive estimation
Citation: Shashi Bhushan, Anoop Kumar, Md Tanwir Akhtar, Showkat Ahmad Lone. Logarithmic type predictive estimators under simple random sampling[J]. AIMS Mathematics, 2022, 7(7): 11992-12010. doi: 10.3934/math.2022668

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Abstract

This study introduces a novel predictive estimation approach of the population mean based on logarithmic type estimators as predictor under simple random sampling. The bias and mean square error of the proffered predictive estimators are examined to the approximation of order one. The efficiency conditions are obtained and the performance of the proffered predictive estimators is examined regarding the contemporary predictive estimators existing till date. Further, a broad computational study is also administered utilizing few real and artificially rendered symmetric and asymmetric populations to exemplify the theoretical results.

References

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