Parameter estimation of exponentiated exponential distribution under selective ranked set sampling
Parameter estimation of exponentiated exponential distribution under selective ranked set sampling

Summary/Abstract: Partial ranked set sampling (PRSS) is a cost-effective sampling method. It is a combinationof simple random sample (SRS) and ranked set sampling (RSS) designs. The PRSS methodallows flexibility for the experimenter in selecting the sample when it is either difficult torank the units within each set with full confidence or when experimental units are notavailable. In this article, we introduce and define the likelihood function of any probabilitydistribution under the PRSS scheme. The performance of the maximum likelihoodestimators is examined when the available data are assumed to have an exponentiatedexponential (EE) distribution via some selective RSS schemes as well as SRS. The suggestedranked schemes include the PRSS, RSS, neoteric RSS (NRSS), and extreme RSS (ERSS).An intensive simulation study was conducted to compare and explore the behaviour of theproposed estimators. The study demonstrated that the maximum likelihood estimators viaPRSS, NRSS, ERSS, and RSS schemes are more efficient than the corresponding estimatorsunder SRS. A real data set is presented for illustrative purposes.

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