3 citations · 7 across the 7 of their papers we have counts for
5 papers · 1 filter
Consistent and Scalable Composite Likelihood Estimation of Probit Models with Crossed Random Effects
Ruggero Bellio, Swarnadip Ghosh, Art B. Owen +1
Estimation of crossed random effects models commonly requires computational costs that grow faster than linearly in the sample size , often as fast as , making them…
Scalable solution to crossed random effects model with random slopes
Disha Ghandwani, Swarnadip Ghosh, Trevor Hastie +1
The crossed random effects model is widely used, finding applications in various fields such as longitudinal studies, e-commerce, and recommender systems, among others. However, th…
Feature Selection in High-dimensional Spaces Using Graph-Based Methods
Swarnadip Ghosh, Somabha Mukherjee, Divyansh Agarwal +3
High-dimensional feature selection is a central problem in a variety of application domains such as machine learning, image analysis, and genomics. In this paper, we propose graph-…
Scalable logistic regression with crossed random effects
Swarnadip Ghosh, Trevor Hastie, Art B. Owen
The cost of both generalized least squares (GLS) and Gibbs sampling in a crossed random effects model can easily grow faster than for observations. Ghosh et al. (2020…
Backfitting for large scale crossed random effects regressions
Swarnadip Ghosh, Trevor Hastie, Art B. Owen
Regression models with crossed random effect errors can be very expensive to compute. The cost of both generalized least squares and Gibbs sampling can easily grow as (or…