Ranked set sampling: an annotated bibliography

Kaur, Amarjit; Patil, G. P.; Sinha, Arun K.; Taillie, C.

doi:10.1007/bf00452930

Cited by 119 publications

(40 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some general themes are parametric and nonparametric inference, balanced and unbalanced designs, efficiency, the effects of imperfect rankings, and regression. The interested reader is referred to Patil et al (1994;, Bohn (1996), Kaur et al (1996), and Muttlak and Al-Saleh (2000) for references pertaining to the RSS literature.…”

Section: Introductionmentioning

confidence: 98%

On Estimating a Population Proportion via Ranked Set Sampling

Terpstra

2004

Biometrical J

View full text Add to dashboard Cite

SummaryThis paper explores the use of the rank set sampling (RSS) protocol as it pertains to the estimation of a population proportion. The maximum likelihood estimator (MLE) and the sample proportion, both based on the RSS data, are discussed and their corresponding asymptotic distributions are derived. Based on these results the MLE is found to be uniformly more efficient than the sample proportion. Nevertheless, both estimators are more efficient than the simple random sample proportion. The greatest gains in efficiency are obtained at the center of the parameter space. Finally, these results remain valid in the presence of judgment error.

show abstract

Section: Introductionmentioning

confidence: 98%

On Estimating a Population Proportion via Ranked Set Sampling

Terpstra

2004

Biometrical J

View full text Add to dashboard Cite

show abstract

“…Moreover, an up-to-date annotated bibliography for RSS can be found in Kaur et al, (1995) and Patil et al (1999). Stokes and Sager (1988) estimate the distribution functions, F(x) say, for a random variable X by the empirical cdf (F*) based on the RSS, which will be given in Section 2.…”

Section: ________________________________________mentioning

confidence: 99%

On Distribution Function Estimation Using Double Ranked Set Samples With Application

Abu–Dayyeh

Samawi

Bani-Hani

2002

J. Mod. App. Stat. Meth.

View full text Add to dashboard Cite

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 this paper DRSS will be used for estimating the distribution function (cdf). The efficiency of the proposed estimators will be obtained when ranking is perfect. Some inference on the distribution function will be drawn based on Kolomgrov-Smirnov statistic. It will be shown that using DRSS will increase the efficiency in this case.

show abstract

“…They prove that the MLE is consistent and demonstrate via simulations that the MLE performs well (when it exists) in the unbalanced case, and in the balanced case it performs better than the edf developed by Stokes and Sager (1988) when both estimators exist and can be compared. For an excellent review of all previous work on ranked set samples, see Kaur et al (1995).…”

Section: Introductionmentioning

confidence: 99%

Smooth non‐parametric estimation of the distribution function from balanced ranked set samples

Gulati

2004

Environmetrics

View full text Add to dashboard Cite

SUMMARYIn certain sampling schemes, such as ranked set sampling, the observed data set can consist entirely of independent order statistics. A balanced ranked set sample consists of n independent cycles, in each of which the experimenter selects k random samples, each of size k. The recorded observation in sample r of cycle i is the rth order statistic, X (r) . Stokes and Sager (1988) have shown that the empirical distribution function based on such samples is an unbiased estimate of the underlying distribution function and is more precise than the empirical distribution function calculated from a simple random sample of the same size. In this article, the properties of the empirical distribution function developed by Stokes and Sager (1988) are further investigated. In addition, a kernel-type estimator of the distribution function is developed. For large n, the smooth estimator is shown to be consistent and asymptotically normal. Finally, small sample properties of the two estimators are investigated and compared via computer simulations.

show abstract

Ranked set sampling: an annotated bibliography

Cited by 119 publications

References 24 publications

On Estimating a Population Proportion via Ranked Set Sampling

On Estimating a Population Proportion via Ranked Set Sampling

On Distribution Function Estimation Using Double Ranked Set Samples With Application

Smooth non‐parametric estimation of the distribution function from balanced ranked set samples

Contact Info

Product

Resources

About