# Design-based estimators of the distribution function in ranked set sampling with an application

@article{Sevil2022DesignbasedEO, title={Design-based estimators of the distribution function in ranked set sampling with an application}, author={Yusuf Can Sevil and Tugba Ozkal Yildiz}, journal={Statistics}, year={2022} }

Empirical distribution functions (EDFs) based on ranked set sampling (RSS) and its modiﬁcations have been examined by many authors. In these studies, the proposed estimators have been investigated for inﬁnite population setting. However, developing EDF estimators in ﬁnite population setting would be more valuable for areas such as environmental, ecological, agricultural, biological, etc. This paper introduces new EDF estimators based on level-0, level-1 and level-2 sampling designs in RSS…

## Figures and Tables from this paper

## References

SHOWING 1-10 OF 37 REFERENCES

Performances of the Distribution Function Estimators Based on Ranked Set Sampling Using Body Fat Data

- Mathematics
- 2020

In this study, an application of empirical distribution function (EDF) estimators based on ranked set sampling (RSS) using real-life data set (body fat data) is illustrated. In this application,…

Design based estimation for ranked set sampling in finite populations

- MathematicsEnvironmental and Ecological Statistics
- 2010

In this paper, we consider design-based estimation using ranked set sampling (RSS) in finite populations. We first derive the first and second-order inclusion probabilities for an RSS design and…

Characterization of a Ranked-Set Sample with Application to Estimating Distribution Functions

- Mathematics
- 1988

Abstract Ranked-set sampling has been shown to provide improved estimators of the mean and variance when actual measurement of the observations is difficult but ranking of the elements in a sample is…

On the Estimation of the Distribution Function Using Extreme and Median Ranked Set Sampling

- Mathematics
- 2001

We study relationships between extreme ranked set samples (ERSSs) and median ranked set sample (MRSS) with simple random sample (SRS). For a random variable X, we show that the distribution function…

On Distribution Function Estimation Using Double Ranked Set Samples With Application

- Mathematics
- 2002

As a variation of ranked set sampling (RSS); double ranked set sampling (DRSS) was introduced by Al-Saleh and Al-Kadiri (2000), and it has been used only for estimating the mean of the population. In…

On distribution function estimation with partially rank-ordered set samples: estimating mercury level in fish using length frequency data

- Mathematics
- 2016

ABSTRACT We study the non-parametric estimation of a continuous distribution function F based on the partially rank-ordered set (PROS) sampling design. A PROS sampling design first selects a random…

Dependence Between Order Statistics in Samples from Finite Population and its Application to Ranked Set Sampling

- Mathematics
- 1998

Let X1, X2,..., Xm, Y1, Y2,..., Yn be a simple random sample without replacement from a finite population and let X(1) ≤ X(2) ≤...≤ X(m) and Y(1) ≤ Y(2) ≤...≤ Y(n) be the order statistics of X1,…

The efficiency of L ranked set sampling in estimating the distribution function

- Mathematics
- 2015

In this paper, L ranked set sampling (LRSS) technique (Al-Nasser in Simul Comput, 6:33–43, 2007) is considered for estimating the distribution function of a random variable. The suggested estimator…

Estimation of a Finite Population Mean and Total Using Population Ranks of Sample Units

- Mathematics
- 2016

This paper introduces new estimators for population total and mean in a finite population setting, where ranks (or approximate ranks) of population units are available before selecting sample units.…

Nonparametric Ranked-set Sampling Confidence Intervals for Quantiles of a Finite Population

- MathematicsEnvironmental and Ecological Statistics
- 2005

Ranked-set sampling from a finite population is considered in this paper. Three sampling protocols are described, and procedures for constructing nonparametric confidence intervals for a population…