Signal-to-noise statistic

In mathematics the signal-to-noise statistic distance between two vectors a and b with mean values $\mu _{a}$ and $\mu _{b}$ and standard deviation $\sigma _{a}$ and $\sigma _{b}$ respectively is:

D_{sn}={(\mu _{a}-\mu _{b}) \over (\sigma _{a}+\sigma _{b})}

In the case of Gaussian-distributed data and unbiased class distributions, this statistic can be related to classification accuracy given an ideal linear discrimination, and a decision boundary can be derived.^[1]

This distance is frequently used to identify vectors that have significant difference. One usage is in bioinformatics to locate genes that are differential expressed on microarray experiments.^[2]^[3]^[4]

Notes

^ Auffarth, B., Lopez, M., Cerquides, J. (2010). Comparison of redundancy and relevance measures for feature selection in tissue classification of CT images. Advances in Data Mining. Applications and Theoretical Aspects. p. 248--262. Springer.
^ Golub, T.R. et al. (1999) Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. Science 286, 531-537,
^ Slonim D.K. et al. (2000) Class Prediction and Discovery Using Gene Expression Data. Procs. of the Fourth Annual International Conference on Computational Molecular Biology Tokyo, Japan April 8 - 11, p263-272
^ Pomeroy, S.L. et al. (2002) Gene Expression-Based Classification and Outcome Prediction of Central Nervous System Embryonal Tumors. Nature 415, 436–442.

This statistics-related article is a stub. You can help Wikipedia by expanding it.

This page was last edited on 31 January 2018, at 14:03

From Wikipedia, the free encyclopedia

See also

Notes