Improved Mean Estimators for Population utilizing Dual Supplementary Characteristics under Simple Random Sampling

Mean estimators using auxiliary Characteristics

Authors

DOI:

https://doi.org/10.21015/vtm.v12i1.1774

Keywords:

Mean; Attributes; Optimum; auxiliary; Efficiency;

Abstract

This paper makes another addition to the existing literature of population mean estimation. An improved family of mean estimators for the population is suggested using simple random sampling and utilizing the information of dual supplementary characteristics. We have conducted an extensive theoretical and numerical investigation of these recommended estimators using the criteria of bias and mean square error. The study provides a derivation of the bias and mean square error of the recommended estimators, approximating up to the first order and compared with the existing estimators. The suggested estimators are also extensively studied numerically and compared using real-world data sets and simulation studies. The theoretical and numerical comparisons show that the estimators suggested in the study are much more efficient than the competitor estimators under all situations i.e. Bahl and Tuteja [21] and other existing estimators in the literature. Therefore, the suggested family of estimators can be applied to real-life problems to obtain better results than the existing mean estimators for the population.

References

Ahmad, S., Arslan, M., Khan, A., and Shabbir, J. (2021). A generalized exponential-type estimator for population mean using auxiliary attributes. Plos one, 16(5):e0246947.

Ahmad, S., Hussain, S., Shabbir, J., Aamir, M., El-Morshedy, M., Ahmad, Z., and Alrajhi, S. (2022). Improved generalized class of

estimators in estimating the finite population mean using two auxiliary variables under two-stage sampling. AIMS Mathematics,

(6):10609–10624.

Ahmad, S., Zahid, E., Shabbir, J., Aamir, M., Emam, W., Tashkandy, Y., and Ahmad, S. (2023). A simulation study: An enhanced generalized class of estimators for estimation of population proportion using twofold auxiliary attribute. Heliyon, 9(6).

Audu, A., Abdulazeez, S., Danbaba, A., Ahijjo, Y., Gidado, A., and Yunusa, M. (2022). Modified classes of regression-type estimators of

the population mean in the presence of auxiliary attributes. Asian Research Journals of Mathematics, 18(1):65–89.

Bahl, S. and Tuteja, R. (1991). Ratio and product type exponential estimators. Journal of information and optimization sciences,

(1):159–164.

Bhushan, S., Kumar, A., Singh, S., and Kumar, S. (2021). An improved class of estimators of population mean under simple random

sampling. Philippine Statist, 70(1):33–47.

Cochran, W. (1940). The estimation of the yields of cereal experiments by sampling for the ratio of grain to total produce. The

journal of agricultural science, 30(2):262–275.

Kumar, A. and Saini, M. (2022). A predictive approach for finite population mean when auxiliary variables are attributes. Thailand Statistician, 20(3):575–584.

Kumar, N. and Singh, H. P. (2023). Estimation of population mean using information on two auxiliary attributes. Lobachevskii Journal of Mathematics, 44(2):838–851.

Kumar, S. and Bhougal, S. (2011). Estimation of the population mean in presence of non-response. Communications for Statistical Applications and Methods, 18(4):537–548.

Lone, S. A., Subzar, M., and Sharma, A. (2021). Enhanced estimators of population variance with the use of supplementary information in survey sampling. Mathematical Problems in Engineering, 2021:1–8.

Malik, S. and Singh, R. (2013). An improved estimator using two auxiliary attributes. Applied mathematics and computation, 219(23):10983–10986.

Naik, V. and Gupta, P. (1996). A note on estimation of mean with known population proportion of an auxiliary character. Jour. Ind. Soc. Agr. Stat, 48(2):151–158.

Rather, K. U. I., Jeelani, M. I., Rizvi, S., and Sharma, M. (2022). A new exponential ratio type estimator for the population means using information on auxiliary attribute. J. Stat. Appl. Pro. Lett, 9:31–42.

Saini, M., Jitendrakumar, B. R., and Kumar, A. (2022). Optimum estimator in simple random sampling using two auxiliary attributes with application in agriculture, fisheries and education sectors. MethodsX,9:101915.

Singh, H. P., Gupta, A., and Tailor, R. (2023a). Efficient class of estimators for finite population mean using auxiliary attribute in stratified random sampling. Scientific Reports, 13(1):10253.

Singh, H. P. and Kumar, S. (2008). A regression approach to the estimation of the finite population mean in the presence of non-response. Australian & New Zealand Journal of Statistics, 50(4):395–408.

Singh, H. P., Malviya, P., and Tailor, R. (2023b). An alternative efficient class of estimators for finite population mean using information on an auxiliary attribute in sample surveys. Journal of Statistical Theory and Practice, 17(1):2.

Subramani, J. and Kumarapandiyan, G. (2012). A class of modified ratio estimators using deciles of an auxiliary variable. Ratio, 10:1.

Subzar, M., Maqbool, S., Raja, T., and Shabeer, M. (2017). A new ratio estimators for estimation of population mean using conventional location parameters. World Applied Sciences Journal, 35(3):377–384.

Subzar, M., Raja, T., Maqbool, S., and Nazir, N. (2016). New alternative to ratio estimator of population mean. International Journal of Agricultural Statistical Sciences, 12(1):221–225.

Wynn, H. (1976). An unbiased estimator under srs of a small change in a proportion. Journal of the Royal Statistical Society. Series D (The Statistician), 25(3):225–228.

Zaman, T. and Kadilar, C. (2019). Novel family of exponential estimators using information of auxiliary attribute. Journal of Statistics and Management Systems, 22(8):1499–1509.

Downloads

Published

2024-05-07

How to Cite

Hussain, M., Zaman, Q., Khan, L., & Sabir, A. (2024). Improved Mean Estimators for Population utilizing Dual Supplementary Characteristics under Simple Random Sampling : Mean estimators using auxiliary Characteristics. VFAST Transactions on Mathematics, 12(1), 202–219. https://doi.org/10.21015/vtm.v12i1.1774

Issue

Vol. 12 No. 1 (2024): January-June

Section

Articles

License

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC-By) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).

                                      

This work is licensed under a Creative Commons Attribution  License CC BY