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>3.0.co;2-#

Cited by 58 publications

(13 citation statements)

References 18 publications

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“…In some application cost of ranking may not be ignored. In that case, one can use a suitable cost function to investigate the trade of between the cost of ranking and extra information gained by proposed designs as in Mode et al (1999) and Nahhas et al (2002).…”

Section: Discussionmentioning

confidence: 99%

Combining ranking information in judgment post stratified and ranked set sampling designs

Öztürk

2011

Environ Ecol Stat

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Judgment post stratified (JPS) and ranked set sampling (RSS) designs rely on the ability of a ranker to assign ranks to potential observations on available experimental units. In many settings, there are often more than one rankers available and each of these rankers provide judgment ranks. This paper proposes two sampling schemes, one for JPS and the other for RSS, to combine the judgment ranks of these rankers to produce a strength of agreement measure for each fully measured unit. This strength measure is used to draw inference for the population mean and cumulative distribution function. The paper shows that the estimators constructed based on this strength measure provide a substantial improvement over the same estimators based on judgment ranking information of a single best ranker.

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Section: Discussionmentioning

confidence: 99%

Combining ranking information in judgment post stratified and ranked set sampling designs

Öztürk

2011

Environ Ecol Stat

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“…For representative examples of applications of RSS methodology in the agricultural, environmental, and ecological sciences literature see Refs 22–28.…”

Section: Applicationsmentioning

confidence: 99%

Ranked set sampling

Wolfe

2010

WIREs Computational Stats

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The most common sampling approach for collecting data from a population with the goal of making inferences about unknown features of the population is a simple random sample (SRS). There is a probabilistic guarantee that each measured observation in an SRS can be considered representative of the population. Despite this assurance, there remains a distinct possibility that a specific SRS might not provide a truly representative picture of the population. With that in mind, statisticians have developed a variety of ways to guard against obtaining such unrepresentative samples. Sampling designs such as stratified sampling, probability sampling, and cluster sampling all provide additional structure on the sampling process to improve the likelihood that the collected sample data do, indeed, provide a good representation of the population. A secondary goal in most data collection settings is to minimize the costs associated with obtaining the data. Ranked set sampling (RSS) is a relatively recent development that addresses both of these issues. It uses additional information from the population to provide more structure to the data collection process and decreases the likelihood of an unrepresentative sample. In addition, it is designed to minimize the number of measured observations required to achieve the desired precision in making inferences. In this article, we provide a general introduction to both balanced and unbalanced RSS, describing the basic approaches for collecting each type of RSS and some of the associated properties. We discuss a number of important factors that affect the performance of RSS procedures. 

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“…() working with RSS methods to estimate the mean of forest, grassland, and other vegetation resources; Mode et al . () 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

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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.

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

Cited by 58 publications

References 18 publications

Combining ranking information in judgment post stratified and ranked set sampling designs

Combining ranking information in judgment post stratified and ranked set sampling designs

Ranked set sampling

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

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