From MFKP_wiki

Jump to: navigation, search


Jack Prins, Don McCormack, Di Michelson, Karen Horrell

edited by: Carroll Croarkin, Paul Tobias, James J. Filliben, Barry Hembree, William Guthrie, Paul Tobias, Ledi Trutna, Jack Prins

Excerpt (Disclaimer)


The following text is a small excerpt from the original publication. Within the general INRMM-MiD goal of indexing useful meta-information on INRMM related publications, this excerpt is intended as a handy summary of some potentially interesting aspects of the publication. However, the excerpt is surely incomplete and some key aspects may be missing or their correct interpretation may require the full publication to be carefully read. Please, refer to the full publication for any detail.


Definitions of order statistics and ranks. For a series of measurements Y1, …, YN, denote the data ordered in increasing order of magnitude by Y〈1〉, …, Y〈N〉. These ordered data are called order statistics. If Y〈j〉 is the order statistic that corresponds to the measurement Yᵢ, then the rank for Yᵢ is j; i.e.,
Y〈j〉 ∼ Yᵢ, rᵢ=j.

Definition of percentiles. Order statistics provide a way of estimating proportions of the data that should fall above and below a given value, called a percentile. The pth percentile is a value, Y〈p〉, such that at most (100p) % of the measurements are less than this value and at most 100(1−p) % are greater. The 50th percentile is called the median.
Percentiles split a set of ordered data into hundredths. (Deciles split ordered data into tenths). For example, 70 % of the data should fall below the 70th percentile.

Estimation of percentiles. Percentiles can be estimated from N measurements as follows: for the pth percentile, set p(N+1) equal to k+d for k an integer, and d, a fraction greater than or equal to 0 and less than 1.
1 ▹ For 0 < k < N, Y(p) = Y〈k〉 + d(Y〈k+1〉 − Y〈k〉)
2 ▹ For k = 0, Y(p) = Y〈1〉
Note that any p ≤ 1/(N+1) will simply be set to the minimum value.
3 ▹ For k ≥ N, Y(p) = Y〈N〉
Note that any p ≥ N/(N+1) will simply be set to the maximum value.

In NIST/SEMATECH e-Handbook of Statistical Methods (2012), 
Key: INRMM:14376208



Available versions (may include free-access full text)…,

Further search for available versions

Search in ResearchGate (or try with a fuzzier search in ResearchGate)
Search in Mendeley (or try with a fuzzier search in Mendeley)

Publication metadata

Bibtex, RIS, RSS/XML feed, Json, Dublin Core

Digital preservation of this INRMM-MiD record

Internet Archive

Meta-information Database (INRMM-MiD).
This database integrates a dedicated meta-information database in CiteULike (the CiteULike INRMM Group) with the meta-information available in Google Scholar, CrossRef and DataCite. The Altmetric database with Article-Level Metrics is also harvested. Part of the provided semantic content (machine-readable) is made even human-readable thanks to the DCMI Dublin Core viewer. Digital preservation of the meta-information indexed within the INRMM-MiD publication records is implemented thanks to the Internet Archive.
The library of INRMM related pubblications may be quickly accessed with the following links.
Search within the whole INRMM meta-information database:
Search only within the INRMM-MiD publication records:
Full-text and abstracts of the publications indexed by the INRMM meta-information database are copyrighted by the respective publishers/authors. They are subject to all applicable copyright protection. The conditions of use of each indexed publication is defined by its copyright owner. Please, be aware that the indexed meta-information entirely relies on voluntary work and constitutes a quite incomplete and not homogeneous work-in-progress.
INRMM-MiD was experimentally established by the Maieutike Research Initiative in 2008 and then improved with the help of several volunteers (with a major technical upgrade in 2011). This new integrated interface is operational since 2014.