155 citations · 955 across the 56 of their papers we have counts for
8 papers · 1 filter
Degrees of freedom for combining regression with factor analysis
Patrick O. Perry, Natesh S. Pillai
In the AGEMAP genomics study, researchers were interested in detecting genes related to age in a variety of tissue types. After not finding many age-related genes in some of the an…
Finite Sample Properties of Adaptive Markov Chains via Curvature
Natesh S. Pillai, Aaron Smith
Adaptive Markov chains are an important class of Monte Carlo methods for sampling from probability distributions. The time evolution of adaptive algorithms depends on past samples,…
A location-mixture autoregressive model for online forecasting of lung tumor motion
Daniel Cervone, Natesh S. Pillai, Debdeep Pati +2
Lung tumor tracking for radiotherapy requires real-time, multiple-step ahead forecasting of a quasi-periodic time series recording instantaneous tumor locations. We introduce a loc…
Bayesian Nonparametric Weighted Sampling Inference
Yajuan Si, Natesh S. Pillai, Andrew Gelman
It has historically been a challenge to perform Bayesian inference in a design-based survey context. The present paper develops a Bayesian model for sampling inference in the prese…
Hypothesis testing for high-dimensional sparse binary regression
Rajarshi Mukherjee, Natesh S. Pillai, Xihong Lin
In this paper, we study the detection boundary for minimax hypothesis testing in the context of high-dimensional, sparse binary regression models. Motivated by genetic sequencing a…
A Function Space HMC Algorithm With Second Order Langevin Diffusion Limit
Michela Ottobre, Natesh S. Pillai, Frank J. Pinski +1
We describe a new MCMC method optimized for the sampling of probability measures on Hilbert space which have a density with respect to a Gaussian; such measures arise in the Bayesi…
Statistical Inference for Stochastic Differential Equations with Memory
Martin Lysy, Natesh S. Pillai
In this paper we construct a framework for doing statistical inference for discretely observed stochastic differential equations (SDEs) where the driving noise has 'memory'. Classi…
Consistency of maximum likelihood estimation for some dynamical systems
Kevin McGoff, Sayan Mukherjee, Andrew Nobel +1
We consider the asymptotic consistency of maximum likelihood parameter estimation for dynamical systems observed with noise. Under suitable conditions on the dynamical systems and…