Unequal Allocation Models for Ranked Set Sampling with Skew Distributions

Kaur, Amarjit; Patil, G. P.; Taillie, C.

doi:10.2307/2533102

Cited by 69 publications

(40 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are many variations of the RSS protocol [3][4][5]. The most fundamental version of RSS is balanced RSS, which features an equal allocation of order statistics in the sample.…”

Section: Introductionmentioning

confidence: 99%

Ranked set sampling for efficient estimation of a population proportion

2005

View full text Add to dashboard Cite

Ranked set sampling (RSS) is a sampling procedure that can be considerably more efficient than simple random sampling (SRS). It involves preliminary ranking of the variable of interest to aid in sample selection. Although ranking processes for continuous variables that are implemented through either subjective judgement or via the use of a concomitant variable have been studied extensively in the literature, the use of RSS in the case of a binary variable has not been investigated thoroughly. In this paper we propose the use of logistic regression to aid in the ranking of a binary variable of interest. We illustrate the application of RSS to estimation of a population proportion with an example based on the National Health and Nutrition Examination Survey III data set. Our results indicate that this use of logistic regression improves the accuracy of the preliminary ranking in RSS and leads to substantial gains in precision for estimation of a population proportion.

show abstract

“…There are many variations of the RSS protocol [3][4][5]. The most fundamental version of RSS is balanced RSS, which features an equal allocation of order statistics in the sample.…”

Section: Introductionmentioning

confidence: 99%

Ranked set sampling for efficient estimation of a population proportion

2005

View full text Add to dashboard Cite

show abstract

“…The following theorem due to Dell and Clutter (1972)and Kaur et al (1997) show that the estimatorμ y(rss) is unbiased for µ y and possesses a lower variance thanμ y(srs) , the sample mean based on an SRSWR sample of the same size n. An unbiased estimator of the variance is also presented here.…”

Section: 2mentioning

confidence: 89%

Improved Estimation from Ranked Set Sampling

Olaomi

Arnab

2014

HJMS

View full text Add to dashboard Cite

Ranked set sampling is used when the measurement or quantification of units of the variable under study is difficult but the ranking of units of sets of small sizes can be done easily by an inexpensive method. Dell and Clutter (1972) showed that the sample mean based on ranked set sample is more efficient than the sample mean based on simple random sample with replacement sampling procedure for estimation of the population mean. In this paper Dell and Clutter estimator has been improved further by using the ranking variable x as an auxiliary variable when µx, the population mean of x is unknown. An empirical investigation based on life data shows all proposed estimators are approximately unbiased and bring gain in efficiency of up to 50 percent.

show abstract

“…The negative effect of the approximate values for the SDs can be aggravated by errors in judgment ranking as an approximate good SD for an order statistics may not be good for a group of units erroneously ascribed to this order statistics. Kaur et al (1997) suggested to make a "near" optimal allocation for the cases in which the underlying statistical distribution is unknown, and hence the SDs of the order statistics. The proposal consists in taking advantage of the knowledge of more easily available characteristics of the population such as skewness, kurtosis and coefficient of variation (CV).…”

Section: Basic Ranked Set Sampling Methodologymentioning

confidence: 99%

“…The aim of this paper is to use the data on surfaces allocated to different crops collected by the Italian Fifth Agricultural Census driven in 2000 and by the Italian Farm Structure Survey driven in year 2003 in order to demonstrate the practical benefits of the unequal allocation rule suggested by Kaur et al (1997) when more skewed correlated variables are of interest.…”

Section: Introductionmentioning

confidence: 99%

Ranked set sampling allocation models for multiple skewed variables: an application to agricultural data

2009

View full text Add to dashboard Cite

The mean of a balanced ranked set sample is more efficient than the mean of a simple random sample of equal size and the precision of ranked set sampling may be increased by using an unbalanced allocation when the population distribution is highly skewed. The aim of this paper is to show the practical benefits of the unequal allocation in estimating simultaneously the means of more skewed variables through real data. In particular, the allocation rule suggested in the literature for a single skewed distribution may be easily applied when more than one skewed variable are of interest and an auxiliary variable correlated with them is available. This method can lead to substantial gains in precision for all the study variables with respect to the simple random sampling, and to the balanced ranked set sampling too.

show abstract

Unequal Allocation Models for Ranked Set Sampling with Skew Distributions

Cited by 69 publications

References 8 publications

Ranked set sampling for efficient estimation of a population proportion

Ranked set sampling for efficient estimation of a population proportion

Improved Estimation from Ranked Set Sampling

Ranked set sampling allocation models for multiple skewed variables: an application to agricultural data

Contact Info

Product

Resources

About