Rational Approaches to Correcting for Multiple Tests

@inproceedings{Tyler2018RationalAT,
  title={Rational Approaches to Correcting for Multiple Tests},
  author={Christopher W. Tyler},
  booktitle={HVEI},
  year={2018}
}
  • C. Tyler
  • Published in HVEI 28 January 2018
  • Computer Science
The logic of the Bonferroni correction for multiple tests, or family-wise error, is to set the criterion to reduce the expected number of erroneous false positives, or Type I errors, below 1. This is a very stringent criterion for false positives in cases where the test may be applied millions of times, and will necessarily introduce a large proportion of false negatives (missed positives, or Type II errors). A proposed solution to this problem is to adjust the criterion for False Discovery… Expand

References

SHOWING 1-10 OF 11 REFERENCES
Controlling the false discovery rate: a practical and powerful approach to multiple testing
SUMMARY The common approach to the multiplicity problem calls for controlling the familywise error rate (FWER). This approach, though, has faults, and we point out a few. A different approach toExpand
A Simple Sequentially Rejective Multiple Test Procedure
This paper presents a simple and widely ap- plicable multiple test procedure of the sequentially rejective type, i.e. hypotheses are rejected one at a tine until no further rejections can be done. ItExpand
An Algebra for the Analysis of Object Encoding
TLDR
The conceptual underpinnings of the study of object encoding are analyzed, some necessary clarifications are drawn in relation to its modality-specific and amodal aspects, and an analytic algebra with specific reference to functional Magnetic Resonance Imaging approaches to the issue of how generic (amodal) object concepts are encoded in the human brain. Expand
THE PROBABLE ERROR OF A MEAN
Any experiment may be regarded as forming an individual of a “population” of experiments which might be performed under the same conditions. A series of experiments is a sample drawn from thisExpand
Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification
TLDR
It is shown that the highest error involves images of dark-skinned women, while the most accurate result is for light-skinned men, in commercial API-based classifiers of gender from facial images, including IBM Watson Visual Recognition. Expand
Fuzzy Sets
TLDR
Improvements are presented for the landmark paper of fuzzy sets for distributive law, convex combination and convex fuzzy sets to help researcher absorb the original paper. Expand
Digital in 2017
  • https://www.linkedin.com/pulse/digitaldata-trends-every-country-world-simonkemp/ (accessed
  • 2017
' Big Data ' dynamic factor models for macroeconomic measurement and forecasting ( Discussion of Reichlin and Watson papers )
  • 2003
Big Data and the Next Wave of Infrastress.https://www.usenix.org/conference /1999-usenix-annual-technical-conference/bigdata-and-next-wave-infrastress-problems (accessed 3/3/2018)
  • 1998
...
1
2
...