Bayesian forecasting in economics and finance: A modern review
The Bayesian statistical paradigm provides a principled and coherent approach to
probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting …
probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting …
Bayesian conjugacy in probit, tobit, multinomial probit and extensions: a review and new results
ABSTRACT A broad class of models that routinely appear in several fields can be expressed
as partially or fully discretized Gaussian linear regressions. Besides including classical …
as partially or fully discretized Gaussian linear regressions. Besides including classical …
Skewed Bernstein–von Mises theorem and skew-modal approximations
Skewed Bernstein-von Mises theorem and skew-modal approximations Page 1 The Annals of
Statistics 2024, Vol. 52, No. 6, 2714–2737 https://doi.org/10.1214/24-AOS2429 © Institute of …
Statistics 2024, Vol. 52, No. 6, 2714–2737 https://doi.org/10.1214/24-AOS2429 © Institute of …
Ordinal logistic regression analysis in determining factors associated with socioeconomic status of household in Tepi town, Southwest Ethiopia
ME Lelisho, AA Wogi, SA Tareke - The Scientific World Journal, 2022 - Wiley Online Library
Background. Socioeconomic status (SES) refers to an individual's or group's social position
or class, which is often determined by a combination of education, income, and occupation …
or class, which is often determined by a combination of education, income, and occupation …
Scalable and accurate variational Bayes for high-dimensional binary regression models
Modern methods for Bayesian regression beyond the Gaussian response setting are often
computationally impractical or inaccurate in high dimensions. In fact, as discussed in recent …
computationally impractical or inaccurate in high dimensions. In fact, as discussed in recent …
Fast variational Bayes methods for multinomial probit models
R Loaiza-Maya, D Nibbering - Journal of Business & Economic …, 2023 - Taylor & Francis
The multinomial probit model is often used to analyze choice behavior. However, estimation
with existing Markov chain Monte Carlo (MCMC) methods is computationally costly, which …
with existing Markov chain Monte Carlo (MCMC) methods is computationally costly, which …
Scalable computation of predictive probabilities in probit models with Gaussian process priors
Predictive models for binary data are fundamental in various fields, and the growing
complexity of modern applications has motivated several flexible specifications for modeling …
complexity of modern applications has motivated several flexible specifications for modeling …
Some properties of the unified skew-normal distribution
RB Arellano-Valle, A Azzalini - Statistical Papers, 2022 - Springer
For the family of multivariate probability distributions variously denoted as unified skew-
normal, closed skew-normal and other names, a number of properties are already known …
normal, closed skew-normal and other names, a number of properties are already known …
Skew-symmetric approximations of posterior distributions
Routinely-implemented deterministic approximations of posterior distributions from, eg,
Laplace method, variational Bayes and expectation-propagation, generally rely on …
Laplace method, variational Bayes and expectation-propagation, generally rely on …
Conjugacy properties of multivariate unified skew-elliptical distributions
The broad class of multivariate unified skew-normal (SUN) distributions has been recently
shown to possess fundamental conjugacy properties. When used as priors for the vector of …
shown to possess fundamental conjugacy properties. When used as priors for the vector of …