8 papers
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…
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…
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…
Tweedie's Formula and Score-Driven Updating
Peter Reinhard Hansen, Chen Tong
Tweedie's formula expresses a conditional mean in terms of a score function, and this connects score-driven models with Bayesian updating. We show that the score-driven update is i…
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…
Moments by Integrating the Moment-Generating Function
Peter Reinhard Hansen, Chen Tong
We introduce a general integral framework for computing fractional, complex, absolute, and logarithmic moments from the moment-generating function (MGF) under explicit regularity c…
Cluster GARCH
Chen Tong, Peter Reinhard Hansen, Ilya Archakov
We introduce a novel multivariate GARCH model with flexible convolution-t distributions that is applicable in high-dimensional systems. The model is called Cluster GARCH because it…
Convolution-t Distributions
Peter Reinhard Hansen, Chen Tong
We introduce a new class of multivariate heavy-tailed distributions that are convolutions of heterogeneous multivariate t-distributions. Unlike commonly used heavy-tailed distribut…