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      Some ratio type predictive estimators for estimation of the average water level of a river using season as an auxiliary attribute

      research-article
      Author(s): a , b , a , *
      Publication date (Electronic):
      Journal: Heliyon
      Publisher: Elsevier
      Keywords: Predictive approach, Study variable, Auxiliary attribute, Bias, Mean square error, Efficiency

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          Abstract

          The study aims to estimate the average water level of a river using an auxiliary attribute under a predictive approach. As the average water level varies in different seasons, the study considers the average water level as a study variable and the season an auxiliary attribute. A real data set of the water level of the Jhelum River at Mangla is obtained. As we found a positive correlation between the average water level and the season, we have suggested some ratio-type predictive estimators for estimating the average water level. The mean square errors (MSE) and bias of the suggested estimators are obtained using the MSE and bias of the corresponding conventional (design-based) estimators. The performance of the proposed predictive estimators, relative to their related existing estimators, has been studied, and improved performance has been established.

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          Ratio and Product Type Exponential Estimators

          Shashi Bahl, R.K. Tuteja (1991)
          Article 0 comments Cited 49 times – based on 0 reviews     Review now
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            The estimation of the yields of cereal experiments by sampling for the ratio of grain to total produce

            W. Cochran (1940)
            In a number of cereal experiments, three on wheat, three on barley and one on oats, the yields of grain and straw per plot were estimated by weighing the total produce on each plot and taking samples, usually from the sheaves, to estimate the ratio of grain to total produce. This paper discusses the sampling errors of this method. The method proved considerably less accurate than was anticipated from previous calculations made by Yates & Zacopanay. Amongst the reasons which are suggested to account for this are the larger sizes of plot and sampling unit used in these experiments and the additional variability introduced by the presence of weeds, undergrowth and moisture. Nevertheless, the method appears to be substantially superior to the older method of cutting small areas from the standing crop, without weighing total produce, only about one-quarter of the number of samples being required to obtain results of equal precision. The samples were taken both by an approximately random process and by grabbing a few shoots haphazardly from each of several sheaves. The grab samples gave on the whole a slightly higher yield of grain, the greatest positive bias being 6%, but were otherwise just as accurate as the random samples. Since the grab samples can be selected and bagged in about one-third of the time required for random samples, their use is recommended for the majority of the samples required in any experiment. The validity of an approximate formula for calculating the variance of a ratio (in the present instance the ratio of grain to total produce) is discussed briefly in an appendix.
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              The Utilization of a Known Coefficient of Variation in the Estimation Procedure

              Donald T Searls (1964)
              Article 0 comments Cited 27 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Muhammad Rafaqat
                Hina Khan
                Journal
                Journal ID (nlm-ta): Heliyon
                Journal ID (iso-abbrev): Heliyon
                Title: Heliyon
                Publisher: Elsevier
                ISSN (Electronic): 2405-8440
                Publication date PMC-release: 14 December 2024
                Publication date Collection: 15 January 2025
                Publication date (Electronic): 14 December 2024
                Volume: 11
                Issue: 1
                Electronic Location Identifier: e41206
                Affiliations
                [a ]Department of Statistics, Government College (GC) University, Lahore, Pakistan
                [b ]Department of Statistics, Mirpur University of Science and Technology (MUST), Mirpur, AJK, Pakistan
                Author notes
                [* ]Corresponding author. hinakhan@ 123456gcu.edu.pk
                Article
                Publisher Item ID: S2405-8440(24)17237-4 Publisher ID: e41206
                DOI: 10.1016/j.heliyon.2024.e41206
                PMC ID: 11721262
                PubMed ID: 39801997
                SO-VID: 2285d11f-0d2e-48ec-8d5b-8545d4d37318
                Copyright © © 2024 Published by Elsevier Ltd.
                License:

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Date received : 19 March 2024
                Date revision received : 11 December 2024
                Date accepted : 12 December 2024
                Categories
                Subject: Research Article

                Keywords: predictive approach,study variable,auxiliary attribute,bias,mean square error,efficiency
                Data availability:
                Keywords: predictive approach, study variable, auxiliary attribute, bias, mean square error, efficiency

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