Ratio-Type Exponential Estimator for the Population Mean at the Current Occasion in the Presence of Non-Response in Successive Sampling
Sankhya B, ISSN: 0976-8394, Vol: 83, Issue: S2, Page: 371-394
2021
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
In many human surveys, information is in most cases not obtained from all the units in the survey even after some call backs. To cope with this problem, survey statisticians usually consider and adopt the non-respondents sub sampling schemes introduced by Hansen and Hurwitz (1946, Jour. Amer. Statist. Assoc., 41, 517–529). The topic which is of practical importance is the estimation of population mean at the current occasion in two- occasion successive sampling in presence of non-response. In this paper, an alternative to Satici and Kadilar (2011, Hacettepe Jour. Math. Statist. 40(1), 115–124) estimator for the population mean at the current occasion in successive sampling having two occasions is suggested under the missing data case. The minimum mean squared error and optimum fraction of a fresh sample of suggested estimator are obtained. The suggested estimator is compared with the estimator due to Singh and Priyanka (2007, Statistics in Transition. 8(2), 273–292) and Satici and Kadilar (2011, Hacettepe Jour. Math. Statist. 40(1), 115–124) and the efficient conditions for the suggested estimator are obtained. It has been shown theoretically that the proposed class of estimators is more efficient than the Singh and Priyanka (2007) and Satici and Kadilar (2011) estimators. The performance of the suggested class of estimators is examined over Singh and Priyanka (2007) and Satici and Kadilar (2011) estimators with the aid of an empirical study.
Bibliographic Details
Springer Science and Business Media LLC
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