Nicholas G. Polson

Publications187
h-index46
Citations12,851
Highly Influential Citations1,357
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Bayesian Analysis of Stochastic Volatility Models
New techniques for the analysis of stochastic volatility models in which the logarithm of conditional variance follows an autoregressive model are developed. A cyclic Metropolis algorithm is used to… Expand
The horseshoe estimator for sparse signals
This paper proposes a new approach to sparsity, called the horseshoe estimator, which arises from a prior based on multivariate-normal scale mixtures. We describe the estimator's advantages over… Expand
Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables
We propose a new data-augmentation strategy for fully Bayesian inference in models with binomial likelihoods. The approach appeals to a new class of Pólya–Gamma distributions, which are constructed… Expand
The Impact of Jumps in Volatility and Returns
This paper examines continuous-time stochastic volatility models incorporating jumps in returns and volatility. We develop a likelihood-based estimation strategy and provide estimates of parameters,… Expand
Bayesian analysis of stochastic volatility models with fat-tails and correlated errors
Abstract The basic univariate stochastic volatility model specifies that conditional volatility follows a log-normal auto-regressive model with innovations assumed to be independent of the… Expand
Particle Learning and Smoothing
Particle learning (PL) provides state filtering, sequential parameter learning and smoothing in a general class of state space models. Our approach extends existing particle methods by incorporating… Expand
Shrink Globally, Act Locally: Sparse Bayesian Regularization and Prediction
We study the classic problem of choosing a prior distribution for a location parameter β = (β1, . . . , βp) as p grows large. First, we study the standard “global-local shrinkage” approach, based on… Expand
Evidence for and the Impact of Jumps in Volatility and Returns
Handling Sparsity via the Horseshoe
TLDR
This paper presents a general, fully Bayesian framework for sparse supervised-learning problems based on the horseshoe prior, which is a member of the family of multivariate scale mixtures of normals and closely related to widely used approaches for sparse Bayesian learning. Expand
A Bayesian analysis of the multinomial probit model with fully identified parameters
We present a new prior and corresponding algorithm for Bayesian analysis of the multinomial probit model. Our new approach places a prior directly on the identified parameter space. The key is the… Expand
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