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 …

Multinomial probit models are routinely-implemented representations for learning how the
class probabilities of categorical response data change with p observed predictors. Although …

Predictive models for binary data are fundamental in various fields, and the growing
complexity of modern applications has motivated several flexible specifications for modeling …

Grouped normal variance mixtures are a class of multivariate distributions that generalize
classical normal variance mixtures such as the multivariate t distribution, by allowing …

Correlated binary response data with covariates are ubiquitous in longitudinal or spatial
studies. Among the existing statistical models, the most well‐known one for this type of data …

Tile low rank representations of dense matrices partition them into blocks of roughly uniform
size, where each off-diagonal tile is compressed and stored as its own low rank factorization …

Limited or censored data are collected in many studies. This occurs for many reasons in
several practical situations, such as limitations in measuring equipment or from an …

Multivariate normal (MVN) probabilities arise in myriad applications, but they are analytically
intractable and need to be evaluated via Monte-Carlo-based numerical integration. For the …

Addressing the statistical challenge of computing the multivariate normal (MVN) probability
in high dimensions holds significant potential for enhancing various applications. One …

We propose a linear-complexity method for sampling from truncated multivariate normal
(TMVN) distributions with high fidelity by applying nearest-neighbor approximations to a …