Consistency tests for key comparison data

Iyer, Hari; Wang, C M; Vecchia, Dominic F.

doi:10.1088/0026-1394/41/4/001

Cited by 27 publications

(18 citation statements)

References 14 publications

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“…Iyer et al [13] outline a fully enumerative computational approach for determining the best LCS. If the N NMIs' measurement results taken together fail the consistency test, examine all possible subsets of N − 1 results (obtained by omitting one NMI's measurement result at a time) and conduct the consistency test on each subset.…”

Section: Full Enumerationmentioning

confidence: 99%

The evaluation of key comparison data: determining the largest consistent subset

Cox

2007

Metrologia

128

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Suppose a single stable travelling standard is circulated around the national metrology institutes (NMIs) participating in a key comparison. Consider the set of data consisting of a measurement result, comprising a measured value and the associated standard uncertainty, provided independently by each such NMI. Each measured value is the corresponding NMI's best estimate of a single stipulated property of the standard. The weighted mean (WM) of the measured values can be formed, the weights being proportional to the reciprocals of the squared standard uncertainties. If this WM is consistent with the measured values according to a statistical test, it can be accepted as a key comparison reference value for the comparison. Otherwise, the WM of a largest consistent subset (LCS) can be determined. The LCS contains as many as possible of those results of participating NMIs that are consistent with the WM of that subset. An efficient approach for determining the LCS having smallest chi-squared value is described, and applied to length, temperature and ionizing radiation comparisons.

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Section: Full Enumerationmentioning

confidence: 99%

The evaluation of key comparison data: determining the largest consistent subset

Cox

2007

Metrologia

128

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“…10 , indicate that low frequency gain boost does not have an effect on the directivity indices for either the cardioid or bidirectional response patterns. Results obtained with the NIST system were compared with the reference values using a conventional approach for judging the quality of measurement results obtained at a laboratory participating in an interlaboratory comparison [ 24 ]. A normalized deviation E n defined by

was calculated for each of the 119 values of Q N measured (seven directional response patterns as a function of seventeen frequency bands).…”

Section: Analysis Of Nist Resultsmentioning

confidence: 99%

NIST System for Measuring the Directivity Index of Hearing Aids under Simulated Real-Ear Conditions

Wagner¹

2013

J. RES. NATL. INST. STAN.

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The directivity index is a parameter that is commonly used to characterize the performance of directional hearing aids, and is determined from the measured directional response. Since this response is different for a hearing aid worn on a person as compared to when it is in a free field, directivity index measurements of hearing aids are usually done under simulated real-ear conditions. Details are provided regarding the NIST system for measuring the hearing aid directivity index under these conditions and how this system is used to implement a standardized procedure for performing such measurements. This procedure involves a sampling method that utilizes sound source locations distributed in a semi-aligned zone array on an imaginary spherical surface surrounding a standardized acoustical test manikin. The capabilities of the system were demonstrated over the frequency range of one-third-octave bands with center frequencies from 200 Hz to 8000 Hz through NIST participation in an interlaboratory comparison. This comparison was conducted between eight different laboratories of members of Working Group S3/WG48, Hearing Aids, established by Accredited Standards Committee S3, Bioacoustics, which is administered by the Acoustical Society of America and accredited by the American National Standards Institute. Directivity measurements were made for a total of six programmed memories in two different hearing aids and for the unaided manikin with the manikin right pinna accompanying the aids. Omnidirectional, cardioid, and bidirectional response patterns were measured. Results are presented comparing the NIST data with the reference values calculated from the data reported by all participating laboratories.

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“…The LCS may be formed using a fully enumerative approach [12]. If the N measured values taken together fail the consistency test, examine all possible subsets of N − 1 values (obtained by omitting one value at a time), and conduct the consistency test on each subset.…”

Section: The Chi-squared Methodsmentioning

confidence: 99%

The application of self-validation to wireless sensor networks

Collett

Cox

Duta

et al. 2008

Meas. Sci. Technol.

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Self-validation is a valuable tool for extending the operating range of sensing systems and making them more robust. Wireless sensor networks suffer many limitations meaning that their efficacy could be greatly improved by self-validation techniques. We present two independently developed data analysis techniques and demonstrate that they can be applied to a wireless sensor network. Using an acoustic ranging application we demonstrate an improvement of more than ten-fold in the uncertainty of a single measurement where multiple sensor readings are appropriately combined. We also demonstrate that of the two methods for determining a largest consistent subset one is more rigorous in dealing with correlation, and the other more suited to time-series data.

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Consistency tests for key comparison data

Cited by 27 publications

References 14 publications

The evaluation of key comparison data: determining the largest consistent subset

The evaluation of key comparison data: determining the largest consistent subset

NIST System for Measuring the Directivity Index of Hearing Aids under Simulated Real-Ear Conditions

The application of self-validation to wireless sensor networks

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