High throughput nonparametric probability density estimation.

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 d

SUBMITTER: Farmer J 

PROVIDER: S-EPMC5947915 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature