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Journal articles on the topic 'Stratified population'

Author: Grafiati

Published: 6 September 2023

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1

Royle, J. Andrew, and Sarah J. Converse. "Hierarchical spatial capture-recapture models: modelling population density in stratified populations." Methods in Ecology and Evolution 5, no. 1 (December 5, 2013): 37–43. http://dx.doi.org/10.1111/2041-210x.12135.

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2

Guo, Zhigang, Dominic M. Tucker, Christopher J. Basten, Harish Gandhi, Elhan Ersoz, Baohong Guo, Zhanyou Xu, Daolong Wang, and Gilles Gay. "The impact of population structure on genomic prediction in stratified populations." Theoretical and Applied Genetics 127, no. 3 (January 24, 2014): 749–62. http://dx.doi.org/10.1007/s00122-013-2255-x.

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3

Hasler, Caren, and Yves Tillé. "Fast balanced sampling for highly stratified population." Computational Statistics & Data Analysis 74 (June 2014): 81–94. http://dx.doi.org/10.1016/j.csda.2013.12.005.

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4

Ivchenko, G. I. "Parameter estimation for a stratified finite population." Journal of Soviet Mathematics 53, no. 6 (March 1991): 552–54. http://dx.doi.org/10.1007/bf01095357.

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5

Ruiz Espejo, Mariano, Housila P. Singh, Miguel Delgado Pineda, and Saralees Nadarajah. "Optimal estimation of population variance using equilibrated stratified sampling from infinite populations." Journal of the Korean Statistical Society 37, no. 4 (December 2008): 375–83. http://dx.doi.org/10.1016/j.jkss.2008.04.001.

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6

SAI, Shido. "Confidence Intervals for Population Mean under Stratified Sampling." Kodo Keiryogaku (The Japanese Journal of Behaviormetrics) 23, no. 2 (1996): 104–21. http://dx.doi.org/10.2333/jbhmk.23.104.

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7

Major, Tamás, Réka Gindele, Zsuzsanna Szabó, Zsuzsanna Kis, László Bora, Natália Jóni, Péter Bárdossy, Tamás Rácz, and Zsuzsanna Bereczky. "The Stratified Population Screening of Hereditary Hemorrhagic Telangiectasia." Pathology & Oncology Research 26, no. 4 (January 26, 2019): 2783–88. http://dx.doi.org/10.1007/s12253-019-00602-7.

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8

Yasmeen, Uzma, Muhammad Noor-ul-Amin, and Muhammad Hanif. "Variance estimation in stratified adaptive cluster sampling." Statistics in Transition New Series 23, no. 1 (March 1, 2022): 173–84. http://dx.doi.org/10.2478/stattrans-2022-0010.

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Abstract In many sampling surveys, the use of auxiliary information at either the design or estimation stage, or at both these stages is usual practice. Auxiliary information is commonly used to obtain improved designs and to achieve a high level of precision in the estimation of population density. Adaptive cluster sampling (ACS) was proposed to observe rare units with the purpose of obtaining highly precise estimations of rare and specially clustered populations in terms of least variances of the estimators. This sampling design proved to be more precise than its more conventional counterparts, including simple random sampling (SRS), stratified sampling, etc. In this paper, a generalised estimator is anticipated for a finite population variance with the use of information of an auxiliary variable under stratified adaptive cluster sampling (SACS). The bias and mean square error expressions of the recommended estimators are derived up to the first degree of approximation. A simulation study showed that the proposed estimators have the least estimated mean square error under the SACS technique in comparison to variance estimators in stratified sampling.

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9

Walsby, Anthony E. "Determining the photosynthetic productivity of a stratified phytoplankton population." Aquatic Sciences 63, no. 1 (March 2001): 18–43. http://dx.doi.org/10.1007/pl00022510.

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10

Mukerjee, Rahul, and Sujit K. Basu. "INVERSE SAMPLING FOR DOMAIN ESTIMATION IN A STRATIFIED POPULATION." Australian Journal of Statistics 35, no. 3 (September 1993): 293–302. http://dx.doi.org/10.1111/j.1467-842x.1993.tb01337.x.

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11

Kaushal, R. P., B. V. S. Sisodia, and U. C. Sud. "Shrunken predictors in stratified sampling under super-population model." Statistics 45, no. 3 (June 2011): 281–91. http://dx.doi.org/10.1080/02331880903546308.

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12

Chavent, Marie, Vanessa Kuentz, Benoıt Liquet, and Jérôme Saracco. "A Sliced Inverse Regression Approach for a Stratified Population." Communications in Statistics - Theory and Methods 40, no. 21 (November 2011): 3857–78. http://dx.doi.org/10.1080/03610926.2010.501940.

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13

Shabbir, Javid, and Sat Gupta. "A New Estimator of Population Mean in Stratified Sampling." Communications in Statistics - Theory and Methods 35, no. 7 (June 2006): 1201–9. http://dx.doi.org/10.1080/03610920600629112.

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14

Heo, Tae-Young, Doo-Ri Lee, and Joong-Jae Cho. "On Statistical Inference of Stratified Population Mean with Bootstrap." Communications for Statistical Applications and Methods 19, no. 3 (May 31, 2012): 405–14. http://dx.doi.org/10.5351/ckss.2012.19.3.405.

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15

Wendell, John P., and Josef Schmee. "Exact Inference for Proportions from a Stratified Finite Population." Journal of the American Statistical Association 91, no. 434 (June 1996): 825–30. http://dx.doi.org/10.1080/01621459.1996.10476950.

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16

Khowaja, Saman, Shazia Ghufran, and M. J. Ahsan. "Estimation of Population Means in Multivariate Stratified Random Sampling." Communications in Statistics - Simulation and Computation 40, no. 5 (March 16, 2011): 710–18. http://dx.doi.org/10.1080/03610918.2010.551014.

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17

Kaur, Parvinder. "On the Estimation of Population Mean in Stratified Sampling." Biometrical Journal 27, no. 1 (1985): 101–5. http://dx.doi.org/10.1002/bimj.4710270115.

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18

Howes, Mick. "Confessions of a Fieldworker-How I stratified a Rural Population." IDS Bulletin 12, no. 4 (May 22, 2009): 40–46. http://dx.doi.org/10.1111/j.1759-5436.1981.mp12004007.x.

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19

Shabbir, Javid, and Sat Gupta. "Some Estimators of Finite Population Variance of Stratified Sample Mean." Communications in Statistics - Theory and Methods 39, no. 16 (August 11, 2010): 3001–8. http://dx.doi.org/10.1080/03610920903170384.

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20

Koyuncu, Nursel, and Cem Kadilar. "On improvement in estimating population mean in stratified random sampling." Journal of Applied Statistics 37, no. 6 (May 11, 2010): 999–1013. http://dx.doi.org/10.1080/02664760903002675.

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21

Polk, Brooke I., Christopher D. Miller, Charles S. Barnes, and Paul J. Dowling. "Age-Stratified Incidence of Aeroallergen Sensitization in a Pediatric Population." Journal of Allergy and Clinical Immunology 139, no. 2 (February 2017): AB157. http://dx.doi.org/10.1016/j.jaci.2016.12.517.

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22

Rodriguez-Gonzalez, Pablo T., Ramiro Rico-Martinez, and Vicente Rico-Ramirez. "An integrated stochastic economic-ecological-social model with stratified-population." Ecological Modelling 368 (January 2018): 15–26. http://dx.doi.org/10.1016/j.ecolmodel.2017.11.012.

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23

Riffe, Daniel, Stephen Lacy, Jason Nagovan, and Larry Burkum. "The Effectiveness of Simple and Stratified Random Sampling in Broadcast News Content Analysis." Journalism & Mass Communication Quarterly 73, no. 1 (March 1996): 159–68. http://dx.doi.org/10.1177/107769909607300114.

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A comparison of simple random, monthly stratified and quarterly/weekly stratified sampling in content analyses of television news used annual “populations” of network newscasts. Different samples were drawn and sample statistics compared with annual parameters. Based on percentages of samples that included population means within one or two standard errors, the most efficient technique was two random days per month.

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24

Muili, Jamiu Olasunkanmi, Ran Vijay Kumar Singh, Gerald Ikechukwu Onwuka, and Ahmed Audu. "Calibration of Stratified Random Sampling with Combined Ratio Estimators." Oriental Journal of Physical Sciences 7, no. 1 (August 30, 2022): 30–36. http://dx.doi.org/10.13005/ojps07.01.05.

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This study considered modification of combined ratio type calibration estimators in stratified random sampling using calibration estimation approaches. The estimators of population mean in stratified random sampling depends on the strata estimated sample means. However, the means are sensitive to the extreme values or outliers in the sample observations of the study variables and strata sizes respectively. A new sets of calibration weights and property of the suggested combined calibration estimators of population mean in stratified sampling were derived. Empirical study through simulation was conducted to investigate the efficiency of the modified combined ratio-type calibration estimators of population mean obtained and the results revealed that the suggested estimators of population mean performed better than some existing estimators considered in the study.

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25

Czerski, Jerzy, and Krystyna Jankowska. "Germination of embryos from stratified and non-stratified seeds and growth of apple seedlings (Malus domestica Borkh cv. "Antonówka")." Acta Societatis Botanicorum Poloniae 50, no. 4 (2014): 595–605. http://dx.doi.org/10.5586/asbp.1981.081.

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The germination of whole seeds, the seeds without coat and isolated embryos of apple cv. "Antonówka Zwykła" after 90 days of cold-stratification was compared with the germination of embryos isolated from non-stratified seeds. They were germinated under 16hrs during a day at temperature 25°C and 20°C during the night. It has been found that after 2 weeks whole stratified seeds germinated in 5 per cent, seeds without coat in 25 per cent and isolated embryos in 98 per cent. Isolated embryos from nun-stratified seeds, after 2 weeks, germinated in the range from 75 to 88 per cent. The results indicate the similar germination ability of embryos isolated from nun-stratified seeds. The seedling populations obtained from embryo's stratified and non-stratified seeds were fully comparable and they evaluated: 1) a wide range of individual differences within population, 2) a similar number of seedlings in each class of shoot length, 3) a similar morphological habitus in each class of shoot length, 4) a similar fresh leaf weight and whole plant increment.

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26

Bhushan, Shashi, Anoop Kumar, Sana Shahab, Showkat Ahmad Lone, and Md Tanwir Akhtar. "On Efficient Estimation of the Population Mean under Stratified Ranked Set Sampling." Journal of Mathematics 2022 (October 6, 2022): 1–20. http://dx.doi.org/10.1155/2022/6196142.

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This paper considers some efficient combined and separate classes of estimators of the population mean in the presence of bivariate auxiliary information under stratified ranked set sampling. The mean square error MSE expressions of the proffered combined and separate classes of estimators are derived to the first order of approximation. The theoretical conditions are obtained under which the proffered combined and separate classes of estimators perform better than the existing combined and separate class of estimators. Subsequently, numerical and simulation studies are performed using real and artificially generated populations. The numerical and simulation results are found to be rewarding, showing the superiority of the proffered estimators over the existing estimators.

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27

Ngesa, Oscar O., G. O. Orwa, R. O. Otieno, and H. M. Murray. "Multivariate Ratio Estimator of the Population Total under Stratified Random Sampling." Open Journal of Statistics 02, no. 03 (2012): 300–304. http://dx.doi.org/10.4236/ojs.2012.23036.

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28

Onyeka, Aloy Chijioke, Chinyeaka Hostensia Izunobi, and Iheanyi Sylvester Iwueze. "Estimation of Population Ratio in Post-Stratified Sampling Using Variable Transformation." Open Journal of Statistics 05, no. 01 (2015): 1–9. http://dx.doi.org/10.4236/ojs.2015.51001.

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29

Plante, Nathalie, Louis-Paul Rivest, and Gilles Tremblay. "Stratified Capture-Recapture Estimation of the Size of a Closed Population." Biometrics 54, no. 1 (March 1998): 47. http://dx.doi.org/10.2307/2533994.

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30

Tailor, Rajesh, and Hilal A. Lone. "Separate Ratio-type Estimators of Population Mean in Stratified Random Sampling." Journal of Modern Applied Statistical Methods 13, no. 1 (May 1, 2014): 223–33. http://dx.doi.org/10.22237/jmasm/1398917580.

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31

Ross, Marvin Jon, James W. Curtsinger, and Robert O. Megard. "Development of population structure in Daphnia clones in a stratified lake." Heredity 77, no. 3 (September 1996): 292–302. http://dx.doi.org/10.1038/hdy.1996.143.

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32

Shabbir, Javid, and Sat Gupta. "On Estimating Finite Population Mean in Simple and Stratified Random Sampling." Communications in Statistics - Theory and Methods 40, no. 2 (December 6, 2010): 199–212. http://dx.doi.org/10.1080/03610920903411259.

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33

Özel, Gamze, Hülya Çingi, and Merve Oğuz. "Separate Ratio Estimators for the Population Variance in Stratified Random Sampling." Communications in Statistics - Theory and Methods 43, no. 22 (November 5, 2014): 4766–79. http://dx.doi.org/10.1080/03610926.2012.729642.

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34

Besga, Ariadna, Borja Ayerdi, Guillermo Alcalde, Alberto Manzano, Pedro Lopetegui, Manuel Graña, and Ana González-Pinto. "Risk Factors for Emergency Department Short Time Readmission in Stratified Population." BioMed Research International 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/685067.

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Background.Emergency department (ED) readmissions are considered an indicator of healthcare quality that is particularly relevant in older adults. The primary objective of this study was to identify key factors for predicting patients returning to the ED within 30 days of being discharged.Methods.We analysed patients who attended our ED in June 2014, stratified into four groups based on the Kaiser pyramid. We collected data on more than 100 variables per case including demographic and clinical characteristics and drug treatments. We identified the variables with the highest discriminating power to predict ED readmission and constructed classifiers using machine learning methods to provide predictions.Results.Classifier performance distinguishing between patients who were and were not readmitted (within 30 days), in terms of average accuracy (AC). The variables with the greatest discriminating power were age, comorbidity, reasons for consultation, social factors, and drug treatments.Conclusions.It is possible to predict readmissions in stratified groups with high accuracy and to identify the most important factors influencing the event. Therefore, it will be possible to develop interventions to improve the quality of care provided to ED patients.

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35

Zhong, B. "Empirical likelihood inference under stratified random sampling using auxiliary population information." Biometrika 87, no. 4 (December 1, 2000): 929–38. http://dx.doi.org/10.1093/biomet/87.4.929.

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36

Koyuncu, Nursel. "Calibration estimator of population mean under stratified ranked set sampling design." Communications in Statistics - Theory and Methods 47, no. 23 (December 5, 2017): 5845–53. http://dx.doi.org/10.1080/03610926.2017.1402051.

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37

Cekim, Hatice Oncel, and Cem Kadilar. "In-type estimators for the population variance in stratified random sampling." Communications in Statistics - Simulation and Computation 49, no. 7 (March 27, 2019): 1665–77. http://dx.doi.org/10.1080/03610918.2019.1577973.

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38

Ivchenko, G. I., S. A. Khonov, and E. A. Ivanov. "Exact maximum likelihood estimator of the structure of a stratified population." Mathematical Notes 62, no. 2 (August 1997): 181–85. http://dx.doi.org/10.1007/bf02355906.

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39

Gross, Christopher, Justin Rabinowitz, and Elizabeth Durante. "Population-based Projections of Ankle Arthrodeses." Foot & Ankle Orthopaedics 3, no. 3 (July 1, 2018): 2473011418S0023. http://dx.doi.org/10.1177/2473011418s00231.

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Category: Ankle Arthritis Introduction/Purpose: Ankle arthrodesis is commonly performed for patients with end stage ankle arthritis. However, with improvements in long-term outcomes following total ankle arthroplasty it is possible that rates of ankle arthrodesis will decrease as utilization of ankle arthroplasty increases. The purpose of this study is to assess the current and future trends of ankle arthrodesis utilization. Methods: National Inpatient Sample (NIS) data from 1997-2014 was used to identify trends in the utilization of ankle arthrodesis. United States Census Bureau data from 1997-2014 was used to identify historical population data and future population estimates. A linear regression model was created using Mathematica v11.3 to project future demand for ankle arthrodesis. The data was stratified to show past and future trends based on gender and age. Results: The overall utilization rate of ankle arthrodesis remained relatively constant from 5897 procedures in 1997 to 5330 procedures in 2014. The overall demand for ankle arthrodesis is expected to increase slightly by 15 percent with 6141 procedures projected in 2045. Stratified by age groups, the number of procedures is predicted to decrease by 75% in ages 18-44 and 6% in ages 45-64, and predicted to increase by 133% in ages 65-84 by 2045. Stratified by gender, ankle arthrodesis is predicted to increase by 47% in males and decrease by 14% in females in 2045. Conclusion: Based on our projection model, the rate of ankle arthrodesis is predicted to stay relatively stable overall but decrease in younger populations. A projected increase in total ankle arthroplasty will likely contribute to decreased utilization of ankle arthrodesis. However, ankle arthrodesis will still be a valuable tool in the surgeon’s armamentarium to treat ankle arthritis.

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40

M, Maria, Ibrahim M. Almanjahie, Muhammad Ismail, and Ammara Nawaz Cheema. "Partial stratified ranked set sampling scheme for estimation of population mean and median." PLOS ONE 18, no. 2 (February 15, 2023): e0275340. http://dx.doi.org/10.1371/journal.pone.0275340.

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Ranked set sampling is an alternative to simple random sampling, which uses the least amount of money and time. The ranked set sampling (RSS) is modified to obtain a more efficient and cost-effective estimator of population parameters. This paper aims to bring a more efficient and cost-effective design than stratified ranked set sampling and simple random sampling. In some distributions, the suggested method used fewer sample units than stratified ranked set sampling and gives a more efficient estimation of population parameters. In symmetric distributions, the proposed design, called "partial stratified ranked set sampling" yields an unbiased estimator of the population mean. The design is illustrated with practical data of COVID-19 confirmed cases.

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41

Tripathi, Gautam. "MOMENT-BASED INFERENCE WITH STRATIFIED DATA." Econometric Theory 27, no. 1 (April 30, 2010): 47–73. http://dx.doi.org/10.1017/s0266466610000125.

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Many data sets used by economists and other social scientists are collected by stratified sampling. The sampling scheme used to collect the data induces a probability distribution on the observed sample that differs from the target or underlying distribution for which inference is to be made. If this effect is not taken into account, subsequent statistical inference can be seriously biased. This paper shows how to do efficient semiparametric inference in moment restriction models when data from the target population are collected by three widely used sampling schemes: variable probability sampling, multinomial sampling, and standard stratified sampling.

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42

Iftikhar, Anum, Shi Hongbo, Sardar Hussain, Sajid Khan, Badr Alnssyan, Javid Shabbir, Sohaib Ahmad, and Muhammad Aamir. "A New Exponential Factor-Type Estimator for Population Distribution Function Using Dual Auxiliary Variables under Stratified Random Sampling." Mathematical Problems in Engineering 2022 (May 10, 2022): 1–14. http://dx.doi.org/10.1155/2022/7673945.

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In this paper, we propose a generalized class of exponential-type estimators for estimating the finite population distribution function using dual auxiliary variables under stratified sampling. The biases and mean squared errors (MSEs) of the proposed class of estimators are derived up to the first order of approximation. The empirical and theoretical study of comparisons is discussed. Four populations are taken for the support of the theoretical findings. It is observed that the proposed class of estimators performs better as compared to all other considered estimators in stratified sampling.

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43

Ruotsalainen, Seppo, and Dag Lindgren. "Stratified sublining: a new option for structuring breeding populations." Canadian Journal of Forest Research 30, no. 4 (April 1, 2000): 596–604. http://dx.doi.org/10.1139/x99-245.

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When structuring a breeding population into sublines, the conventional approach is to assign parents to sublines randomly, so that each subline has approximately the same genetic value. By using deterministic infinitesimal model we study an alternative, stratified sublining system, where sublines are initially formed by positive assortative grouping of parents according to their breeding values. Stratified and random allocation to sublines are compared by evaluating the genetic quality of the seed orchards that each approach can provide. The seed orchards were established by selecting first the best individual from each subline and then a given best proportion from them. The greater among-subline variance in stratified sublining led to higher genetic gain in resulting seed orchards than did random sublining. For the case studied, stratified sublining gave considerably more genetic gain than random sublining, over 15% more, making it an interesting alternative that deserves further consideration and study.

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44

Al-Kassab, Mowafaq Muhammed. "Using Artificial Bee Colony Algorithm to Determine the Optimum Stratum Boundaries Depending on Neyman Allocation." Journal of Advance Research in Computer Science & Engineering (ISSN: 2456-3552) 3, no. 3 (March 31, 2016): 19–28. http://dx.doi.org/10.53555/nncse.v3i3.428.

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In stratified sampling, in order to gain more precision than other methods of sampling, a heterogeneous population is divided into subpopulations, each of which is internally homogeneous. As a result the main problem arising in stratified sampling is to obtain the optimum boundaries. Several numerical and computational methods have been developed for this purpose. Some apply to highly skewed populations and some apply to any kind of populations .This paper proposes an ABC algorithm to solve the problem of stratum boundary while distributing the sample size according to Neyman Allocation method. The ABC algorithm is tested on two groups of populations and a comparative study with Genetic Algorithm (GA) of Keskintürk and Er (2007), Kozak’s (2004), Lavallée and Hidiroglou’s (1988) and Dalenius and Hodges(1959) methods have been implemented. The numerical results show the ability of the proposed algorithm tofind the optimal stratified boundaries for a set of standard populations and various standard test functions compared with other algorithms.

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45

Barnes, R. F. W., A. Blom, M. P. T. Alers, and K. L. Barnes. "An estimate of the numbers of forest elephants in Gabon." Journal of Tropical Ecology 11, no. 1 (February 1995): 27–37. http://dx.doi.org/10.1017/s0266467400008361.

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ABSTRACTThe number of elephants living in the forests of Gabon was estimated by a stratified dung survey. The forest zone was stratified according to the distribution of the human population. The density of dung-piles was estimated for each stratum by the line-transect method. The total number of dung-piles in the forests of Gabon was estimated to be 52.43 × 106 ± 13.25 × 106. This gave an elephant estimate of 61,800 ±20,200. This survey shows Gabon to have one of the largest elephant populations on the continent.

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46

Fatima, Ummatul, Shazia Ghufran, and M. J. Ahsan. "The problem of optimum allocation in multivariate stratified sampling." South Pacific Journal of Natural and Applied Sciences 30, no. 1 (2012): 65. http://dx.doi.org/10.1071/sp12007.

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Generally, sample surveys are multivariate in nature where multiple response are obtained on every unit selected in a sample, that is, more than one characteristics are defined on each and every unit of the population. While dealing with a multivariate stratified population, to workout an allocation that is optimum for all characteristics is almost impossible unless the characteristics are highly correlated. Some compromise must be allowed to obtain an allocation that is optimum, in some sense, for all the characteristics. Since such allocations are based on some compromise criteria they are known as compromise allocations. This paper deals with the problem of obtaining an optimum allocation in multivariate stratified sampling design.

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47

Mehta (Ranka), Nitu, and Latika Sharma. "Generalized Power Transformation Estimators in Stratified Ranked Set Sampling using Auxiliary Information." International Journal of Bio-resource and Stress Management 14, Mar, 3 (March 25, 2023): 492–97. http://dx.doi.org/10.23910/1.2023.3365a.

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Auxiliary variable is commonly used in survey sampling to improve the precision of estimates. Whenever there is auxiliary information available, the researchers want to utilize it in the method of estimation to obtain the most efficient estimator. Stratified simple random sampling (SSRS) is used in certain types of surveys because it combines the conceptual simplicity of simple random sampling (SRS) with potentially significant gains in efficiency. It is a convenient technique to use whenever we wish to ensure that our sample is representative of the, population and also to obtain separate estimates for parameters of each sub domain of the population. Stratified Ranked Set Sampling combines the advantages of Stratification and Ranked set sampling (RSS) to obtain an unbiased estimator for the population mean, with potentially significant gains in efficiency. Under Stratified ranked set sampling scheme, we have suggested two general estimators using power transformation to estimate the population mean of the study variable. These methods are highly beneficial to the estimation based on Stratified Simple Random Sampling (SSRS). The first order approximation to the bias and mean square error (MSE) of the proposed estimators are obtained. Theoretically, it is shown that these suggested estimators are more efficient than the estimators in Stratified simple random sampling. A numerical illustration is also included to demonstrate the merits of the proposed estimator using SRSS over the corresponding estimators in SSRS.

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48

Bhushan, Shashi, Anoop Kumar, Usman Shahzad, Amer Ibrahim Al-Omari, and Ibrahim Mufrah Almanjahie. "On Some Improved Class of Estimators by Using Stratified Ranked Set Sampling." Mathematics 10, no. 18 (September 9, 2022): 3283. http://dx.doi.org/10.3390/math10183283.

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In this manuscript, we propose the combined and separate difference and ratio type estimators of population mean using stratified ranked set sampling. Additionally, several well-known estimators are identified as the sub-class of the suggested estimators. The characteristics of the suggested estimators have been analyzed and their effective performances are compared with the prominent estimators existing till date. Moreover, to prove the credibility of the theoretical findings, an extensive empirical study is administered over some real and hypothetically yielded symmetric and asymmetric populations.

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49

Unal, Melih, Ozkan Kose, Hasan Arik, Ferhat Guler, Baver Acar, and Halil Yuksel. "Hand grip strength: Age and gender stratified normative data in Anatolian population." Hand and Microsurgery 6, no. 3 (2018): 1. http://dx.doi.org/10.5455/handmicrosurg.257058.

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50

Jin, Kai, Shu-Yuan Zhang, Li-Wen Li, Yang-Fan Zou, Bin Wu, Liang Xia, and Cai-Xing Sun. "Prognosis of Oligodendroglioma Patients Stratified by Age: A SEER Population-Based Analysis." International Journal of General Medicine Volume 14 (December 2021): 9523–36. http://dx.doi.org/10.2147/ijgm.s337227.

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