High throughput nonparametric probability density estimation

PloS One
Jenny Farmer, Donald Jacobs

Abstract

In high throughput applications, such as those found in bioinformatics and finance, it is important to determine accurate probability distribution functions despite only minimal information about data characteristics, and without using human subjectivity. Such an automated process for univariate data is implemented to achieve this goal by merging the maximum entropy method with single order statistics and maximum likelihood. The only required properties of the random variables are that they are continuous and that they are, or can be approximated as, independent and identically distributed. A quasi-log-likelihood function based on single order statistics for sampled uniform random data is used to empirically construct a sample size invariant universal scoring function. Then a probability density estimate is determined by iteratively improving trial cumulative distribution functions, where better estimates are quantified by the scoring function that identifies atypical fluctuations. This criterion resists under and over fitting data as an alternative to employing the Bayesian or Akaike information criterion. Multiple estimates for the probability density reflect uncertainties due to statistical fluctuations in random samples. Sc...Continue Reading

References

Jun 28, 2011·Journal of Computational Biology : a Journal of Computational Molecular Cell Biology·B KnappP Bauer
Dec 10, 2013·ISA Transactions·Payman HajihosseiniBehzad Moshiri
Jan 15, 2016·Journal of Chemical Theory and Computation·Lucas Sawle, Kingshuk Ghosh
Apr 6, 2016·IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control·Ivan M Rosado-MendezTimothy J Hall
Apr 11, 2017·PloS One·Joakim MunkhammarJesper Rydén

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Citations

Jan 10, 2021·Entropy·Carlos Alberto de Braganca PereiraAgatha Sacramento Rodrigues
Feb 21, 2021·Scientific Reports·Tyler GrearDonald J Jacobs

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Software Mentioned

KDE
MATLAB

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