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.
Converting high-dimensional regression to high-dimensional conditional density estimation , volume =
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A semi-supervised kernel two-sample test integrates unlabeled covariate data to achieve asymptotic normality under the null, higher power than standard kernel tests, and consistency against fixed and local alternatives.
The first circumgalactic dust reddening measurement from Rubin DP1 data finds A_V proportional to r_perp to the -1.8 power within 120 kpc, consistent with prior SDSS/KiDS/DES results despite 1000x smaller area and fainter foreground sample.
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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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A Semi-Supervised Kernel Two-Sample Test
A semi-supervised kernel two-sample test integrates unlabeled covariate data to achieve asymptotic normality under the null, higher power than standard kernel tests, and consistency against fixed and local alternatives.
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A First Measurement of Circumgalactic Dust Reddening from Only 4.6 deg$^2$ of the Rubin Observatory's DP1
The first circumgalactic dust reddening measurement from Rubin DP1 data finds A_V proportional to r_perp to the -1.8 power within 120 kpc, consistent with prior SDSS/KiDS/DES results despite 1000x smaller area and fainter foreground sample.