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Generalized estimator for computation of population mean under neutrosophic ranked set technique: An application to solar energy data

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Abstract

Classical statistics is used so far deals with crisp or determinate types of data only but it fails if there is uncertainty in data. Neutrosophic statistics is a generalization of classical as well as fuzzy statistics and the best substitute for classical as well as fuzzy statistics to deal with such uncertainty in data. This manuscript proposes a neutrosophic ranked set sampling (NeRSS) method and then a generalized estimator for estimating the population means under indeterminacy using neutrosophic subsidiary information. We have also given neutrosophic ranked set ratio and product-type estimators which are the same as the member class of estimators from the proposed estimator. The expressions for bias and mean square error (MSE) of the proposed generalized class of estimators have been derived to the first order of approximation and compare over its member estimators and unbiased estimator through MSE criterion. The proposed estimator has shown superiority over its member estimators, unbiased estimator, and over corresponding generalized estimator under neutrosophic simple random sampling (NeSRS). To show the performance of the proposed methodology, an empirical as well simulation study through R Studio have been carried out.

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Data availability

The author confirms that the data supporting the findings of this study are available within the article and can be seen in detail through Almaraashi (2017), Aslam, and Algarni (2020). The data can be accessed by anyone from these two references.

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Acknowledgements

Authors are heartily thankful to Editors and anonymous learned reviewers for their valuable comments which have made substantial improvements to bring the original manuscript to its present form.

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Authors and Affiliations

  1. Department of Mathematics and Computing, Indian Institute of Technology (ISM) Dhanbad, Dhanbad, 826004, Jharkhand, India

    Gajendra K. Vishwakarma & Abhishek Singh

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  1. Gajendra K. Vishwakarma
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  2. Abhishek Singh
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Correspondence to Abhishek Singh.

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Communicated by Anibal Tavares de Azevedo.

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Appendices

Appendix A

A flow chart diagram for the proposed method of estimation.

figure a

Appendix B

See Figs. 1 and 2.

Fig. 1
figure 1

REs of the proposed estimators (Neutrosophic vs Classical)

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Fig. 2
figure 2

REs of the proposed estimators (Neutrosophic vs Classical), where \(T_{\left[ u \right]N} = t0\); \(T_{\left[ R \right]N} = tr\); \(T_{\left[ P \right]N} = tp\); \(T_{\left[ 1 \right]N} = t1\); \(T_{\left[ 2 \right]N} = t2\); \(T_{\left[ 3 \right]N} = t3\); \(T_{\left[ 4 \right]N} = t4\); \(T_{\left[ 5 \right]N} = t5\); \(T_{\left[ 6 \right]N} = t6\); \(T_{\left[ 7 \right]N} = t7\); \(T_{\left[ 8 \right]N} = t8\); \(T_{\left[ g \right]N} = tg\)

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Vishwakarma, G.K., Singh, A. Generalized estimator for computation of population mean under neutrosophic ranked set technique: An application to solar energy data. Comp. Appl. Math. 41, 144 (2022). https://doi.org/10.1007/s40314-022-01820-7

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