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cs.CV2026

Spatiotemporal Recognition of Abdominal Wall Layers During Optical Trocar Entry

Shreeram Athreya, Andrew Melehy, Carter Prentice +4

Objective. To evaluate whether spatial context and temporal modeling improve tissue recognition during optical trocar entry, where multiple abdominal-wall tissues may be visible si…

math.ST2026

Bartlett Couplings of the Onion and Vine LKJ Samplers

Peter Reinhard Hansen

The extended-onion and C-vine constructions of Lewandowski, Kurowicka and Joe (2009) are standard methods for sampling from the LKJ_n(eta) distribution on correlation matrices. We…

stat.CO2026

voigtinference: Exact likelihood calculus and conditional attribution for the Voigt profile

Peter Reinhard Hansen, Chen Tong

Fast, accurate algorithms for the Faddeeva function w(z), and hence the Voigt profile K(x,a), have existed for four decades, and analytic first derivatives are available in some im…

math.PR2026

Correlation Matrices in High Dimensions: Volume, Spectrum, and Extremes

Peter Reinhard Hansen

The set of correlation matrices, known as the elliptope, has volume decaying at the super-exponential rate . We characterize where this van…

econ.EM2026

Split-Session Cluster GARCH for Overnight and Intraday Returns: The Role of Tail Heterogeneity

Xinxian Chen, Peter Reinhard Hansen, Chen Tong

We propose the Split-Session Cluster GARCH model for heavy-tailed multivariate dependence among asset returns decomposed into overnight and intraday components. The model uses conv…

econ.EM2026

Exact Likelihood Inference and Robust Filtering for Gauss-Cauchy Convolution Models

Peter Reinhard Hansen, Chen Tong

The convolution of a Gaussian and a Cauchy distribution, known as the Voigt distribution, is widely used in spectroscopy and provides a natural framework for modeling heavy-tailed…

math.NT2026

The Parabolic Mellin Transform: Gamma and Zeta Integral Representations

Peter Reinhard Hansen, Chen Tong

We introduce the Parabolic Mellin Transform (PMT), defined by , where and . Under the substitution , the ver…