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- W4221145015 abstract "There is a need for new models for characterizing dependence in multivariate data. The multivariate Gaussian distribution is routinely used, but cannot characterize nonlinear relationships in the data. Most non-linear extensions tend to be highly complex; for example, involving estimation of a non-linear regression model in latent variables. In this article, we propose a relatively simple class of Ellipsoid-Gaussian multivariate distributions, which are derived by using a Gaussian linear factor model involving latent variables having a von Mises-Fisher distribution on a unit hyper-sphere. We show that the Ellipsoid-Gaussian distribution can flexibly model curved relationships among variables with lower-dimensional structures. Taking a Bayesian approach, we propose a hybrid of gradient-based geodesic Monte Carlo and adaptive Metropolis for posterior sampling. We derive basic properties and illustrate the utility of the Ellipsoid-Gaussian distribution on a variety of simulated and real data applications." @default.
- W4221145015 created "2022-04-03" @default.
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- W4221145015 date "2022-01-20" @default.
- W4221145015 modified "2023-09-26" @default.
- W4221145015 title "Curved factor analysis with the Ellipsoid-Gaussian distribution" @default.
- W4221145015 doi "https://doi.org/10.48550/arxiv.2201.08502" @default.
- W4221145015 hasPublicationYear "2022" @default.
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