Improved memory-type ratio estimator for population mean in stratified random sampling under linear and non-linear cost functions

被引：0
作者：
Pal A. [1 ]
Varshney R. [2 ]
Yadav S.K. [2 ]
Zaman T. [3 ]
机构：
[1] Department of Statistics, Multanimal Modi College, Modinagar, CCS University, Uttar Pradesh, Meerut
[2] Department of Statistics, School of Physical and Decision Sciences, Babasaheb Bhimrao Ambedkar University, Uttar Pradesh, Lucknow
[3] Department of Statistics, Faculty of Science, Çankiri Karatekin University, Çankiri
来源：
Soft Computing | 2024年 / 28卷 / 13-14期
关键词：
AINLPP; Genetic programming technique; Linear cost function; Non-linear cost function; Ratio estimator; Stratified random sampling;
D O I：
10.1007/s00500-023-09598-4
中图分类号：
学科分类号：
摘要：
This paper offers an improved memory-type ratio estimator in stratified random sampling under linear and non-linear cost functions. The issue is given as all integer non-linear programming problems (AINLPPs). The sampling properties mainly the bias and the mean squared error of the introduced estimator are derived up to the first order of approximation. The optimum value of the characterizing scalar is obtained by the Lagrange method of maxima–minima. The least value of the MSE of the suggested estimator is also obtained for this optimum value of the charactering constant. The suggested estimator is compared both theoretically and empirically with the competing estimators. Under this setup, the optimum allocation with mean square error of the suggested estimator is attained, and the estimator is compared to other comparable estimators. The AINLPP is solved using the genetic programming approach, which is applied to both actual and simulated data sets from a bivariate normal distribution. © The Author(s) 2024.
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页码：7739 / 7754
页数：15
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