Universal sample size invariant measures for uncertainty quantification in density estimation

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Jacobs, D. J., Farmer, J., Merino, Z., & Merino, Z. (2019). Universal sample size invariant measures for uncertainty quantification in density estimation. Entropy.
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Faculty, Previously, we developed a high throughput non-parametric maximum entropy method (PLOS ONE, 13(5): e0196937, 2018) that employs a log-likelihood scoring function to characterize uncertainty in trial probability density estimates through a scaled quantile residual (SQR). The SQR for the true probability density has universal sample size invariant properties equivalent to sampled uniform random data (SURD). Alternative scoring functions are considered that include the Anderson-Darling test. Scoring function effectiveness is evaluated using receiver operator characteristics to quantify efficacy in discriminating SURD from decoy-SURD, and by comparing overall performance characteristics during density estimation across a diverse test set of known probability distributions.

Details
Title

Universal sample size invariant measures for uncertainty quantification in density estimation

Author(s)

Jacobs, Donald J.
Farmer, Jenny
Merino, Zach
Merino, Zach

UNC Charlotte Affiliate Type

Faculty

Extent

1 online resource

Date Created

2019

Genre

articles

Resource Type

text

Abstract

Previously, we developed a high throughput non-parametric maximum entropy method (PLOS ONE, 13(5): e0196937, 2018) that employs a log-likelihood scoring function to characterize uncertainty in trial probability density estimates through a scaled quantile residual (SQR). The SQR for the true probability density has universal sample size invariant properties equivalent to sampled uniform random data (SURD). Alternative scoring functions are considered that include the Anderson-Darling test. Scoring function effectiveness is evaluated using receiver operator characteristics to quantify efficacy in discriminating SURD from decoy-SURD, and by comparing overall performance characteristics during density estimation across a diverse test set of known probability distributions.

Subjects--Topics

Statistics
Entropy

Is Part Of

Entropy

Language

English

Publisher's Version

doi:10.3390/e21111120

Permalink

http://hdl.handle.net/20.500.13093/work:965

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