다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Order Restricted Statistical Inference.

(1993). Statistical Methods for Survival Data Analysis. Technometrics: Vol. 35, No. 1, pp. 101-101.

Statistical Methods for Survival Data Analysis

Ranked set sampling (RSS) is an approach to data collection and analysis that continues to stimulate substantial methodological research. It has spawned a number of related methodologies that are active research arenas as well, and it is finally beginning to find its way into significant applications beyond its initial agricultural-based birth in the seminal paper by McIntyre (1952). In this paper, we provide an introduction to the basic concepts underlying ranked set sampling, in general, with specific illustrations from the one- and two-sample settings. Emphasis is on the breadth of the ranked set sampling approach, with targeted discussion of the many options available to the researcher within the RSS paradigm. The paper also provides a thorough bibliography of the current state of the field and introduces the reader to some of the most promising new methodological extensions of the RSS approach to statistical data analysis.

https://downloads.hindawi.com/archive/2012/568385.pdf

Ranked Set Sampling: Its Relevance and Impact on Statistical Inference

(2005). Ranked Set Sampling: Theory and Applications. Technometrics: Vol. 47, No. 1, pp. 100-101.

Ranked Set Sampling: Theory and Applications

This article studies the properties of the maximum likelihood estimator of the population proportion in ranked set sampling with extreme ranks. The maximum likelihood estimator is described and its...

Using ranked set sampling with extreme ranks in estimating the population proportion.

A more efficient proportion estimator in ranked set sampling

When measuring units are expensive or time consuming, while ranking them is relatively easy and inexpensive, it is known that ranked set sampling (RSS) is preferable to simple random sampling (SRS). Many authors have suggested several extensions of RSS. As a variation, Al-Saleh and Al-Kadiri [Double ranked set sampling, Statist. Probab. Lett. 48 (2000), pp. 205–212] introduced double ranked set sampling (DRSS) and it was extended by Al-Saleh and Al-Omari [Multistage ranked set sampling, J. Statist. Plann. Inference 102 (2002), pp. 273–286] to multistage ranked set sampling (MSRSS). The entropy of a random variable (r.v.) is a measure of its uncertainty. It is a measure of the amount of information required on the average to determine the value of a (discrete) r.v.. In this work, we discuss entropy estimation in RSS design and aforementioned extensions and compare the results with those in SRS design in terms of bias and root mean square error (RMSE). Motivated by the above observed efficiency, we continue...

Efficiency of ranked set sampling in entropy estimation and goodness-of-fit testing for the inverse Gaussian law

Rank-based sampling methods are applicable in settings where precise measurements are expensive, but small sets of units can be accurately ranked at negligible cost. This article introduces one such a design, called multistage pair ranked set sampling. It mitigates ranking burden associated with a competitor scheme, namely multistage ranked set sampling. The mean estimator in multistage pair ranked set sampling is unbiased, and under perfect rankings has variance no larger than its simple random sampling counterpart. Although the suggested mean estimator is outperformed by its multistage ranked set sampling analog in terms of precision under perfect rankings, the situation may be reversed if cost considerations are taken into account. The methodology is illustrated using a medical dataset.

https://journals.sagepub.com/doi/pdf/10.1177/0962280217720473

Efficient body fat estimation using multistage pair ranked set sampling.

In this paper, we consider the problem of estimating the population proportion in pair ranked set sampling design. An unbiased estimator for the population proportion is proposed, and its theoretical properties are studied. It is shown that the estimator is more (less) efficient than its counterpart in simple random sampling (ranked set sampling). Asymptotic normality of the estimator is also established. Application of the suggested procedure is illustrated using a data set from an environmental study.

M. Mahdizadeh

Papers

Using ranked set sampling with extreme ranks in estimating the population proportion.

A more efficient proportion estimator in ranked set sampling

Efficiency of ranked set sampling in entropy estimation and goodness-of-fit testing for the inverse Gaussian law

Efficient body fat estimation using multistage pair ranked set sampling.

Estimating the population proportion in pair ranked set sampling with application to air quality monitoring