155 citations · 955 across the 56 of their papers we have counts for
9 papers · 1 filter
Edge universality of correlation matrices
Natesh S. Pillai, Jun Yin
Let be a rectangular data matrix with independent real-valued entries satisfying and $\mathbb {…
On a Class of Shrinkage Priors for Covariance Matrix Estimation
Hao Wang, Natesh S. Pillai
We propose a flexible class of models based on scale mixture of uniform distributions to construct shrinkage priors for covariance matrix estimation. This new class of priors enjoy…
Relevant statistics for Bayesian model choice
J. -M. Marin, N. Pillai, C. P. Robert +1
The choice of the summary statistics used in Bayesian inference and in particular in ABC algorithms has bearings on the validation of the resulting inference. Those statistics are…
Universality of covariance matrices
Natesh S. Pillai, Jun Yin
In this paper we prove the universality of covariance matrices of the form where is an rectangular matrix with independent real val…
Gradient Flow from a Random Walk in Hilbert Space
Natesh S. Pillai, Andrew M. Stuart, Alexandre H. Thiery
Consider a probability measure on a Hilbert space defined via its density with respect to a Gaussian. The purpose of this paper is to demonstrate that an appropriately defined Mark…
Regularity of laws and ergodicity of hypoelliptic SDEs driven by rough paths
Martin Hairer, Natesh S. Pillai
We consider differential equations driven by rough paths and study the regularity of the laws and their long time behavior. In particular, we focus on the case when the driving noi…
Optimal scaling and diffusion limits for the Langevin algorithm in high dimensions
Natesh S. Pillai, Andrew M. Stuart, Alexandre H. Thiéry
The Metropolis-adjusted Langevin (MALA) algorithm is a sampling algorithm which makes local moves by incorporating information about the gradient of the logarithm of the target den…
Lack of confidence in ABC model choice
Christian P. Robert, Jean-Marie Cornuet, Jean-Michel Marin +1
Approximate Bayesian computation (ABC) have become a essential tool for the analysis of complex stochastic models. Earlier, Grelaud et al. (2009) advocated the use of ABC for Bayes…
Why approximate Bayesian computational (ABC) methods cannot handle model choice problems
Christian Robert, Jean-Michel Marin, Natesh S. Pillai
Approximate Bayesian computation (ABC), also known as likelihood-free methods, have become a favourite tool for the analysis of complex stochastic models, primarily in population g…