A Bayesian global Fréchet regression method is introduced via a Fréchet Bayes rule that reduces the problem to scalar tasks, allows prior-data interpolation, and remains valid under moment conditions using weak conditional expectations.
Iteratively reweighted least squares for maximum likelihood estimation, and some robust and resistant alternatives
4 Pith papers cite this work, alongside 7 external citations. Polarity classification is still indexing.
4
Pith papers citing it
7
external citations · external index
years
2026 4representative citing papers
A full-spectrum fitting approach using K-U-Th-radon-cosmic background templates plus a Monte Carlo detector model maintains energy calibration of gamma detectors over -25 °C to +50 °C without active temperature control.
A robust, heteroskedastic matrix factorization method generalizes PCA to handle per-feature uncertainties, missing data, and outlier detection via Student-t likelihood iterative reweighting.
ldmppr is an R package providing tools to model, simulate from, and assess goodness-of-fit for location-dependent marked point processes.
citing papers explorer
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Bayesian Global Fr\'echet Regression via Weak Conditional Expectations
A Bayesian global Fréchet regression method is introduced via a Fréchet Bayes rule that reduces the problem to scalar tasks, allows prior-data interpolation, and remains valid under moment conditions using weak conditional expectations.
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Automatic calibration of gamma-ray detectors deployed in uncontrolled environments
A full-spectrum fitting approach using K-U-Th-radon-cosmic background templates plus a Monte Carlo detector model maintains energy calibration of gamma detectors over -25 °C to +50 °C without active temperature control.
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Robust Heteroskedastic Matrix Factorization: A Generalization of PCA that Flags Outliers and Handles Missing Data
A robust, heteroskedastic matrix factorization method generalizes PCA to handle per-feature uncertainties, missing data, and outlier detection via Student-t likelihood iterative reweighting.
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ldmppr: Location Dependent Marked Point Processes in R
ldmppr is an R package providing tools to model, simulate from, and assess goodness-of-fit for location-dependent marked point processes.