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In this paper, we proposed highly efficient ranked set sampling schemes to estimate the population mean. First, we proposed a new single-stage sampling scheme which we called new neoteric ranked set sampling. Second, we proposed a two-stage methods based on the systematic ranked set sampling and the new neoteric ranked set sampling. The performance of the proposed methods is compared with that of competitive two-stage methods through a Monte Carlo simulation study using various popular symmetric and asymmetric statistical distributions. The results show that the newly proposed methods are more efficient in estimating the population mean than the existing methods. The proposed methods are illustrated on data of the diameter and height of pine trees.

We study the problem of obtaining confidence intervals (CIs) within a parametric framework under different ranked set sampling (RSS) designs. This is an important research issue since it has not yet been adequately addressed in the RSS literature. We focused on evaluating CIs based on a recently developed parametric bootstrap approach, and the asymptotic maximum likelihood CIs under simple random sampling (SRS) was taken as the counterpart. A comprehensive simulation study was carried out to evaluate the accuracy and precision of the CIs. We have considered as sampling designs the paired RSS, neoteric RSS, and double RSS, besides the original RSS and SRS. Different estimation methods and bootstrap CIs were evaluated. In addition, the robustness of the CIs to imperfect ranking was evaluated by inducing varied levels of ranking errors. The simulated results allowed us to identify accurate bootstrap CIs based on RSS and some of its extensions, which outperform the usual asymptotic or bootstrap CIs based on SRS in terms of accuracy (coverage rate) and/or precision (average width).

Sample selection is one of the most important factors in estimating the unknown parameters of distributions, as it saves time, saves effort, and gives the best results. One of the challenges is deciding on a suitable distribution estimate technique and adequate sample selection to provide the best results in comparison with earlier research. The method of moments (MOM) was decided on to estimate the unknown parameters of the Gumbel distribution, but with four changes in the sample selection, which were simple random sample (SRS), ranked set sampling (RSS), maximum ranked set sampling (MRSS), and ordered maximum ranked set sampling (OMRSS) techniques, due to small sample sizes. The MOM is a traditional method for estimation, but it is difficult to use when dealing with RSS modification. RSS modification techniques were used to improve the efficiency of the estimators based on a small sample size compared with the usual SRS estimator. A Monte Carlo simulation study was carried out to compare the estimates based on different sampling. Finally, two datasets were used to demonstrate the adaptability of the Gumbel distribution based on the different sampling techniques.

It is formally shown that ranked set sampling (RSS) and its variations produce estimators enjoying with many interesting properties. Although the problem of nonparametric quantile estimation has drawn much attention under RSS, it has not been addressed under RSS variations. To fill this gap, this article deals with the problem of estimating the population quantiles based on RSS and its variations which are pair RSS, and MiniMax RSS. Two quantiles estimators are proposed and their asymptotic distributions are also derived. Taking into account the information provided by the concomitant variable, additional three quantiles estimators are also introduced. The comprehensive simulation-based results reveal the superiority of all the proposed estimators, particularly the concomitant-based estimators, over their counterpart in simple random sampling among most of the considered cases as long as the ranking quality is fairly good. Finally, an empirical data set is also used to illustrate application of the proposed procedures.

This paper is concerned with the estimation problem using maximum likelihood method of estimation for the unknown parameters of exponetiated gumbel distribution based on neoteric ranked set sampling (NRSS) as a new modification of the usual ranked set sampling (RSS) technique. Numerical study is conducted to compare NRSS as a dependent ranked set sampling technique, with RSS, and median ranked set sampling as independent sampling techniques, and then the performance of RSS and its modifications will be compared with simple random sampling based on their mean square errors and efficiencies.

A new variety of Ranked Set Sampling (RSS), namely Induced Generalized Record Ranked Set Sampling (IGRRSS), is introduced. In the proposed methodology, ranking is implemented by considering generalized (k) record values on the auxiliary variable X from each sequence of units. The selected units are further screened for measuring the variable of primary interest Y. Further, we propose estimators based on IGRRSS for the unknown parameters associated with the variable Y when the parent bivariate distribution belongs to the Morgenstern family of distributions. The proposed sampling scheme is utilized to collect primary data on the usable timber volume Y based on the ranking of units by generalized (2) record values on tree height X of acacia trees. Accordingly, Morgenstern type bivariate logistic distribution has been modelled for the distribution of the population random vector (X, Y) and estimated the average usable timber volume of the population.

In this paper, we derive the likelihood function of the neoteric ranked set sampling (NRSS) as dependent in sampling method and double neoteric ranked set sampling (DNRSS) designs as combine between independent sampling method in the first stage and dependent sampling method in the second stage and they compared for the estimation of the parameters of the inverse Weibull (IW) distribution. An intensive simulation has been made to compare the one and the two stages designs. The results showed that likelihood estimation based on ranked set sampling (RSS) as independent sampling method, NRSS and DNRSS designs provide more efficient estimators than the usual simple random sampling design. Moreover, the DNRSS is slightly more efficient than the NRSS and RSS designs in the case of estimating the IW distribution parameters.

We present a novel methodology for estimating the parameters of a finite mixture model (FMM) based on partially rank-ordered set (PROS) sampling and use it in a fishery application. A PROS sampling design first selects a simple random sample of fish and creates partially rank-ordered judgement subsets by dividing units into subsets of prespecified sizes. The final measurements are then obtained from these partially ordered judgement subsets. The traditional expectation–maximization algorithm is not directly applicable for these observations. We propose a suitable expectation–maximization algorithm to estimate the parameters of the FMMs based on PROS samples. We also study the problem of classification of the PROS sample into the components of the FMM. We show that the maximum likelihood estimators based on PROS samples perform substantially better than their simple random sample counterparts even with small samples. The results are used to classify a fish population using the length-frequency data.

Ranked set sampling (RSS) was first introduced by McIntyre (1952) as a competitor of simple random sampling (SRS), the most common tool in the statistical methods. When the sample size is not large enough, it may be difficult to obtain a representative subset from the population based on SRS, but RSS and its generalizations overcome to this shortcoming. These sampling schemes usually work based on judgment ranking of the sample units. The present paper investigates the performance of the mentioned schemes when the underlying distribution is the well-known Azzalini's skew-normal (SN) distribution. It also answers to an important question, that is, which kind of rank-based sampling methods is appropriate when the parent distribution is SN? To this end, the maximum (penalized) likelihood estimation as well as the method of moments are applied as the estimation approaches of the skewness parameter of SN distribution. Comparison of the estimators is carried out via their mean squared error and the Pitman measure of closeness criteria through a simulation study. Results show that the suggested scheme is highly dependent on the sign of the skewness parameter. Keywords: Maximum penalized estimation, Median ranked set sampling, Modified ranked set sampling, Ranked set sampling, Skew-Normal distribution. AMS Subject Classification: 62C10, 62F07

In this paper we consider Bayesian estimation based on bivariate ranked set sample, in which units are ranked based on measurements made on an easily and exactly measurable auxiliary variable X which is correlated with the study variable Y. We obtain Bayes estimator for the scale parameter of the study variate Y, when (X, Y ) follows a Morgenstern type bivariate Rayleigh distribution. The Bayes estimators are considered based on bivari-ate ranked set sampling, extreme ranked set sampling and maximum ranked set sampling with unequal sample. The accuracy of estimation methods in this paper is illustrated using simulation study. Finally, a real data set is analyzed.