Universal Sample Size Invariant Measures for Uncertainty Quantification in Density Estimation

Universal Sample Size Invariant Measures for Uncertainty Quantification in Density Estimation

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 probabil...

Ausführliche Beschreibung

Bibliographische Detailangaben
Hauptverfasser: Jenny Farmer, Zach Merino, Alexander Gray, Donald Jacobs
Format: Artikel
Sprache:English
Veröffentlicht: MDPI AG 2019-11-01
Schriftenreihe:Entropy
Schlagworte:
density estimation
distribution free
non-parametric statistical test
decoy distributions
size invariance
scaled quantile residual
maximum entropy method
scoring function
outlier detection
overfitting detection
Online Zugang:https://www.mdpi.com/1099-4300/21/11/1120
Beschreibung
Zusammenfassung: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.
ISSN:1099-4300

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