Ranked set sampling for ecological research: accounting for the total costs of sampling

Mode, Nicolle A.; Conquest, Loveday L.; Marker, David A.

doi:10.1002/(sici)1099-095x(199903/04)10:2<179::aid-env346>3.3.co;2-r

Cited by 11 publications

(22 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use of RSS in environmentics is closely related with the seminal paper of McIntyre (1955). Some examples are Evans (1967), who applied the RSS to regeneration surveys in areas direct-seeded to longleaf pine, Martin et al (1980) applying RSS for estimating shrub phytomass in Appalachian Oak forests; Johnson et al (1993) working with RSS methods to estimate the mean of forest, grassland, and other vegetation resources; Mode et al (1999) investigated under which conditions the RSS becomes a cost-effective sampling method for ecological and environmental field studies where the rough but cheap measurement has a cost.…”

Section: Introductionmentioning

confidence: 99%

Ratio estimators of the population mean with missing values using ranked set sampling

Al-Omari

Bouza

2014

Environmetrics

View full text Add to dashboard Cite

The existence of missing observations (MO) is commonly solved by using imputation methods. There are ratio‐based methods for estimating the population mean, while using simple random sampling (SRS), when MO are present. Considering the existence of MO and using of ranked set sampling, we develop a study of the estimation of a population mean using ratio‐based methods. The mean square errors, bias, and gain in accuracy formulas of the suggested estimators are derived. The suggested estimators are compared with their SRS counterpart both theoretically and numerically. Copyright © 2014 John Wiley & Sons, Ltd.

show abstract

Section: Introductionmentioning

confidence: 99%

Ratio estimators of the population mean with missing values using ranked set sampling

Al-Omari

Bouza

2014

Environmetrics

View full text Add to dashboard Cite

show abstract

“…As errors may get involved while ordering due to dependence on the ranker , s judgment, Dell and Clutter [4] showed that the RSS estimator of a population mean remains unbiased and is at least as efficient as the SRS estimator with the same number of quantifications. Patil et al [5] provided a bibliography for RSS; Patil et al [6] showed its use for estimating the level of concentration of Poly Chlorinated Biphenyls (PCB) in soil, while Johnson et al [7] described its applications in vegetation research and Mode et al [8] discussed its relevance for ecological research. Under the nonparametric set up Bohn and Wolfe [9][10], and Hettmansperger [11], and Koti and Babu [12] used the sampling method for developing MRSS based nonparametric methods.…”

Section: Introductionmentioning

confidence: 99%

Weibull-Bayesian analysis based on ranked set sampling

Sadek¹,

Alharbi²

2014

IJASP

View full text Add to dashboard Cite

Most of estimation methods reported in the literature are based on simple random sampling (SRS), which to certain extent is considerably less effective in estimating the parameters as compared to a new sampling technique, ranked set sampling (RSS) and its modifications. In this Paper we address the problem of Bayesian estimation of the parameters for Weibull distribution, based on ranked set sampling. Two loss functions have been studied: (i) the squared-error loss function as symmetric loss function, (ii) the linex loss function as asymmetric loss function. Different estimates are compared using simulations for illustrative purposes.

show abstract

“…The RSS consists of these m selected units. For recent work, consult Patil et al (1999), Al-Saleh and Al-Kadiri (2000), Al-Saleh and Samawi (2000), Chen (2000), Samawi (2001), Zheng and Al-Saleh (2002) and Al-Saleh and Al-Omari (2002).…”

Section: Introductionmentioning

confidence: 99%

“…For example, in situations where quantifications entail destruction of units as in uprooting of plants. Also, analytical procedures such as spectroscopy can be used to quantify several contaminants at once (Patil et al 1994); also Mode et al (1999) and Al-Saleh and Zheng (2002) In this article, the ERSS is combined with BVRSS to obtain a more applicable procedure namely the Bivariate Extreme Ranked Set Sampling (BVERSS). In section 2, the procedure is described and some fundamental properties will be given.…”

Section: Introductionmentioning

confidence: 99%

Estimation Using Bivariate Extreme Ranked Set Sampling With Application To The Bivariate Normal Distribution

Al-Saleh

Samawi

2004

J. Mod. App. Stat. Meth.

View full text Add to dashboard Cite

In this article, the procedure of bivariate extreme ranked set sampling (BVERSS) is introduced and investigated as a procedure of obtaining more accurate samples for estimating the parameters of bivariate populations. This procedure takes its strength from the advantages of bivariate ranked set sampling (BVRSS) over the usual ranked set sampling in dealing with two characteristics simultaneously, and the advantages of extreme ranked set sampling (ERSS) over usual RSS in reducing the ranking errors and hence in being more applicable. The BVERSS procedure will be applied to the case of the parameters of the bivariate normal distributions. Illustration using real data is also provided.

show abstract

Ranked set sampling for ecological research: accounting for the total costs of sampling

Cited by 11 publications

References 0 publications

Ratio estimators of the population mean with missing values using ranked set sampling

Ratio estimators of the population mean with missing values using ranked set sampling

Weibull-Bayesian analysis based on ranked set sampling

Estimation Using Bivariate Extreme Ranked Set Sampling With Application To The Bivariate Normal Distribution

Contact Info

Product

Resources

About