Search PubMed⌕ Search

PubMed · 11550950

Ranked set sampling with unequal samples.

Abstract

A ranked set sampling procedure with unequal samples (RSSU) is proposed and used to estimate the population mean. This estimator is then compared with the estimators based on the ranked set sampling (RSS) and median ranked set sampling (MRSS) procedures. It is shown that the relative precisions of the estimator based on RSSU are higher than those of the estimators based on RSS and MRSS. An example of estimating the mean diameter at breast height of longleaf-pine trees on the Wade Tract in Thomas County, Georgia, is presented.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

D S Bhoj. 2001. Ranked set sampling with unequal samples.. https://doi.org/10.1111/j.0006-341x.2001.00957.x

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Intensive short courses in biostatistics for fellows and physicians.

At both of our universities we teach (with colleagues) introductory courses in statistics for fellows and physicians. We do not expect that those taking these courses will be able to do their own statistical work, but rather the intention is for them to 'learn the language' and to facilitate future collaboration. Basic principles of study design are introduced in the courses, as well as some of the most common statistical procedures. We will discuss the factors (what works and what does not) that may contribute to a successful course, a comparison to other courses, and our self-evaluation strategy. Finally, we will cover the financial arrangements that we have made when teaching these courses.

Biometry↗

Likelihood methods for measuring statistical evidence.

Focused on interpreting data as statistical evidence, the evidential paradigm uses likelihood ratios to measure the strength of statistical evidence. Under this paradigm, re-examination of accumulating evidence is encouraged because (i) the likelihood ratio, unlike a p-value, is unaffected by the number of examinations and (ii) the probability of observing strong misleading evidence is naturally low, even for study designs that re-examine the data with each new observation. Further, the controllable probabilities of observing misleading and weak evidence provide assurance that the study design is reliable without affecting the strength of statistical evidence in the data. This paper illustrates the ideas and methods associated with using likelihood ratios to measure statistical evidence. It contains a comprehensive introduction to the evidential paradigm, including an overview of how to quantify the probability of observing misleading evidence for various study designs. The University Group Diabetes Program (UGDP), a classic and still controversial multi-centred clinical trial, is used as an illustrative example. Some of the original UGDP results, and subsequent re-analyses, are presented for comparison purposes.

Biometry↗