Bayesian Analysis of Stochastic Volatility Models
- E. Jacquier, Nicholas G. Polson, Peter E. Rossi
- Mathematics
- 1 October 1994
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…
The horseshoe estimator for sparse signals
- C. Carvalho, Nicholas G. Polson, James G. Scott
- Mathematics
- 1 June 2010
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…
Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables
- Nicholas G. Polson, James G. Scott, J. Windle
- Mathematics
- 2 May 2012
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…
The Impact of Jumps in Volatility and Returns
- Michael S. Johannes, Bjørn Eraker, Nicholas G. Polson
- Economics, Mathematics
- 1 November 2000
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,…
Bayesian analysis of stochastic volatility models with fat-tails and correlated errors
- E. Jacquier, Nicholas G. Polson, Peter E. Rossi
- Economics
- 1 September 2004
Shrink Globally, Act Locally: Sparse Bayesian Regularization and Prediction
- Nicholas G. Polson, James G. Scott, B. Clarke, C. Severinski
- Mathematics
- 19 January 2012
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…
Handling Sparsity via the Horseshoe
- C. Carvalho, Nicholas G. Polson, James G. Scott
- Computer ScienceInternational Conference on Artificial…
- 2009
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.
Particle Filtering
- Michael S. Johannes, Nicholas G. Polson
- Environmental Science
- 2006
This chapter provides an overview of particle filters. Particle filters generate approximations to filtering distributions and are commonly used in non-linear and/or non-Gaussian state space models.…
A Monte Carlo Approach to Nonnormal and Nonlinear State-Space Modeling
- B. Carlin, Nicholas G. Polson, D. Stoffer
- Mathematics
- 1 June 1992
Abstract A solution to multivariate state-space modeling, forecasting, and smoothing is discussed. We allow for the possibilities of nonnormal errors and nonlinear functionals in the state equation,…
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