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The Journal of Information and Optimization Sciences (JIOS) is a world leading journal publishing high quality, rigorously peer-reviewed original research in all mathematically-oriented theoretical and applied topics in information sciences, optimization sciences and related areas since 1980. Subjects include but are not limited to:
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Estimation of finite population mean in sample surveys: A new estimator
*Surya K. PalCorresponding authorsuryakantpal6676@gmail.comDepartment of Mathematics Sharda School of Basic Sciences and Research Sharda University Greater Noida 201310 Uttar Pradesh IndiaDepartment of Mathematics Sharda School of Basic Sciences and Research Sharda University Greater Noida, Uttar Pradesh, 201310, IndiaView full profile →
, Sagir A. Mahmudamsagir@fptb.edu.ngDepartment of Mathematics Sharda School of Basic Sciences and Research Sharda University Greater Noida 201310 Uttar Pradesh IndiaDepartment of Mathematics Sharda School of Basic Sciences and Research Sharda UniversityGreater Noida, Uttar Pradesh, 201310, IndiaView full profile →
, Madan M. Guptamadangupta22@gmail.comDepartment of Statistics Meerut College Meerut 250002 Uttar Pradesh IndiaView full profile →
, Housila P. Singhhspujn@gmail.comSchool of Studies in Statistics Vikram University Ujjain 456010 Uttar Pradesh IndiaSchool of Studies in Statistics Vikram UniversityUjjain, Madhya Pradesh, 456010, IndiaView full profile →
, Ramkrishna S. Solankiramkssolanki@gmail.comDepartment of Mathematics and Statistics College of Agriculture Waraseoni Balaghat 481331 Madhya Pradesh IndiaView full profile →
* Corresponding author · click or hover a name for details
Utilizing supplementary information in simple random sampling, this research paper discussed a new method for finding the finite population mean of a predictive variable, and the properties of the suggested method have been investigated. The suggested estimator’s advantages over traditional estimators are demonstrated using theoretical asymptotic techniques and empirical analysis. The recommended estimator outperforms the customary unbiased, ratio, product, and regression estimators, as well as many other known population mean estimators.
[1] Cochran, W.G. (1940). The estimation of the yields of cereal experiments by sampling for the ratio of grain to total produce. Journal of Agricultural Science 30: 262-275.[2] Das, A.K. and Tripathi, T.P. (1978). Use of auxiliary information in estimating the finite population variance. Sankhya C 40(2): 139-148.[3] Hansen, M.H., Hurwitz, W.N. and Madow, W.G. (1953). Sample Survey Methods and Theory. John Wiley & Sons: New York (USA).[4] Kadilar, C. and Cingi, H. (2006). An improvement in estimating the population mean by using the correlation coefficient. Hacettepe Journal of Mathematics and Statistics 35(1): 103-109.[5] Koyuncu, N. and Kadilar, C. (2010). On the family of estimators of population mean in stratified random sampling. Pakistan Journal of Statistics 26(2): 427-443.[6] Murthy, M.N. (1964). Product method of estimation. Sankhya A 26(1): 69-74.[7] Sharma. P and Pal, S.K. (2018): Estimation of population mean in presence of random non-response. Journal of Statistics and Management Systems, Volume 21(6), 1105-1119.[8] Robson, D.S. (1957). Applications of multivariate polykays to the theory of unbiased ratio-type estimation. Journal of the American Statistical Association 52(280): 511-522.[9] Singh, H.P. and Tailor, R. (2003). Use of known correlation coefficient in estimating the finite population means. Statistics in Transition 6(4): 555-560.[10] Singh, H.P., Tailor, R., Tailor, R. and Kakran, M.S. (2004). An improved estimator of population mean using power transformation. Journal of the Indian Society of Agricultural Statistics 58(2): 223-230.[11] Singh, S. (2003). Advanced Sampling Theory with Applications: How Michael “selected” Amy. Kluwer Academic Publishers: The Netherlands.[12] Sisodia, B.V.S. and Dwivedi, V.K. (1981). A modified ratio estimator using coefficient of variation of auxiliary variable. Journal of the Indian Society of Agricultural Statistics 33(1): 13-18.[13] Pal,S.K. and Pal, A.K. (2021): The impact of increase in COVID-19 cases with exceptional situation to SDG: Good health and Well-being. Journal of Statistics & Management Systems, 24(1), 209-228.[14] Pal, S.K. and Singh, H.P. (2018): Improved estimators of the finite universe variance and mean using ancillary variable in sample surveys, International Journal of Mathematics and Computation, 29 (2), 58-70.[15] Pal, S.K. and Singh, H.P.(2022): An efficient new approach for estimating the general parameter using auxiliary variable in sample surveys. Afrika Matematika 33, 1-26. https://doi.org/10.1007/s13370-022-00983-0.[16] Pal, S.K., Singh, H.P. & Solanki, R.S.(2020): A new efficient class of estimators of finite population mean in simple random sampling. Afrika Matematika, 31, 595–607 . https://doi.org/10.1007/s13370-019-00745-5.[17] Srivastava, S.K. and Jhajj, H.S. (1980). A class of estimators using auxiliary information for estimating finite population variance. Sankhya C 42(1-2): 87-96.[18] Mukherjee, S.,Venkataiah, C., Baral, M. M. & Pal, S.K. (2022): Analyzing the factors that will impact the supply chain of the COVID-19 vaccine: A structural equation modeling approach, Journal of Statistics and Management Systems, 25 (3), 1-17.[19] Upadhyaya, L.N. and Singh, H.P. (1999). Use of transformed auxiliary variable in estimating the finite population mean. Biometrical Journal 41 (5): 627-636.[20] Watson, D.J. (1937). The estimation of leaf area in field crops. Journal of Agricultural Science 27: 474– 483.[21] Yan, Z. and Tian, B. (2010): Ratio method to the mean estimation using coefficient of skewness of auxiliary variable. ICICA (II) CCIS 106: 103–110.
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