arXiv subjects

Mitra Kharabati

Publications and source records attributed to Mitra Kharabati.

2 recordsLinked to original sources

arXiv2026

Variational Bayesian Sparse Negative Binomial Regression

Count data with overdispersion and high-dimensional predictors pose significant challenges in modern applications. While negative binomial regression offers a flexible modeling framework, existing Bayesian approaches rely on computationally expensive MCMC methods that become impractical in high-dimensional settings. This paper develops a variational Bayesian framework for sparse negative binomial regression using horseshoe and continuous spike-and-slab priors. Our proposed methods achieve estimation accuracy and variable selection performance comparable to MCMC benchmarks while offering substantial computational savings over MCMC. Extensive simulations demonstrate that the negative binomial specification is essential for overdispersed data, as Poisson-based approaches exhibit substantial performance degradation under overdispersion. Conversely, our methods remain robust when the data are Poisson, making them a safer default choice. Applications to real benchmark datasets further confirm the practical utility of our approach.

stat.ME↗

arXiv2026

Variational Inference for Sparse Poisson Regression

We have utilized the non-conjugate Variational Bayesian (VB) method for the problem of the sparse Poisson regression model. To provide approximate conjugacy in the model, the likelihood is approximated by a quadratic function, yielding conjugacy between the approximation component and the Gaussian prior on the regression coefficient. Three sparsity-enforcing priors (Laplace, Continuous Spike and Slab, and Bernoulli) are used for this problem. The proposed models are compared with each other, the associated MCMC models, and two frequentist sparse Poisson methods (LASSO and SCAD) to evaluate their estimation, prediction, and sparsity performance. In a simulation study, the proposed VB methods closely approximate the posterior parameter distribution while achieving significantly faster computation than benchmark MCMC methods. Using several benchmark count response data sets, the prediction performance of the proposed methods is evaluated in real-world applications.

stat.ME↗