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
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative 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.
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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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.