Partial ranked set sampling (PRSS) is a cost-effective sampling method. It is a combination of simple random sample (SRS) and ranked set sampling (RSS) designs. The PRSS method allows flexibility for the experimenter in selecting the sample when it is either difficult to rank the units within each set with full confidence or when experimental units are not available. In this article, we introduce and define the likelihood function of any probability distribution under the PRSS scheme. The performance of the maximum likelihood estimators is examined when the available data are assumed to have an exponentiated exponential (EE) distribution via some selective RSS schemes as well as SRS. The suggested ranked 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 the proposed estimators. The study demonstrated that the maximum likelihood estimators via PRSS, NRSS, ERSS, and RSS schemes are more efficient than the corresponding estimators under SRS. A real data set is presented for illustrative purposes.
Recently, Nedjar and Zeghdoudi [6] proposed a new lifetime model called gamma Lindley distribution. Roozegar and Nadarajah [8] introduced some notes around gamma Lindley distribution including only some statistical properties and estimations depending on the same probability density function which proposed by Nedjar and Zeghdoudi [6]. In fact, the model proposed by Nedjar and Zeghdoudi [6] is not a probabilistic model. Further, some of its fundamental properties as well as parameter estimations are incorrect. Hence, all corrections which proposed by Roozegar and Nadarajah [8] are also incorrect. On the other hand, Messaadia and Zeghdoudi [5] proposed only one remark around the parameter space of gamma Lindley distribution which proposed by Nedjar and Zeghdoudi [6], but the mathematical properties and estimations are still wrong for both Nedjar and Zeghdoudi [6] and Roozegar and Nadarajah [8]. Because, Messaadia and Zeghdoudi [5] did not discuss any corrections around quantile function, entropies, estimation methods, simulation and data analysis. In this paper, several corrections including probability density function, quantile function, entropies, estimation the model parameters using the maximum likelihood estimation and moment estimation methods and simulation are discussed in-detail because the previous three papers not make a benefit to the readers, especially in practical field.
We reveal and investigate the Topp-Leone (TL) Weibull G (TLWG) family, a new generator of continuous lifespan distributions, as a consequence of this article. However, various of its statistical characteristics have been proposed. Examine the TLWG family, a new source of continuous lifespan distributions. The parameters of maximum likelihood (MLL) estimations are predicted. We can see the significance and versatility of the recommended family of algorithms while using the TLW exponential model as an example of the new recommendation family with the help of two real-world examples.
In this article we introduce and investigate the weighted Shanker distribution, a weighted version of Shanker's (2015) Shanker distribution. To obtain the form of the weighted Shanker distribution, which is shown as a generalization of the Shanker distribution, a special non-negative weight function is considered. The statistical properties of the weighted Shanker distribution are investigated. We propose a maximum likelihood method for estimating the weighted version's unknown parameter. To observe the pattern of estimates for different sample sizes, a sample generation algorithm and a Monte Carlo simulation study are prepared.
In this work, we show how to estimate stress strength (SS) reliability when the stress (Y) and strength (X) distributions are generalized exponentials with a common scale parameter. The SS reliability estimator is considered in view of neoteric ranked set sampling (NRSS) and median ranked set sampling (MRRS). We acquire an estimate of the reliability (R) when such samples of the stress and strength random variables are gathered using the same NRSS technique. Furthermore, the reliability estimator is derived when the stress distribution data are in the pattern of MRSS with just an odd/even set size and the strength distribution data are derived from NRSS and vice versa. The simulation results are used to evaluate and understand the adequacy of a variety of estimators for the suggested schemes. Based on our simulated results, we found that NRSS-based stress strength reliability estimates are more efficient than MRSS-based stress strength reliability estimates. The analysis of real-world data is used to implement the recommended estimators.
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