Sparsity Inducing Prior Distributions for Correlation Matrices of Longitudinal Data

Sparsity Inducing Prior Distributions for Correlation Matrices through the Partial Autocorrelations

Modeling a correlation matrix R can be a difficult statistical task due to both the positive definite and the unit diagonal constraints. Because the number of parameters increases quadratically in the dimension, it is often useful to consider a sparse parameterization. We introduce a pair of prior distributions on the set of correlation matrices for longitudinal data through the partial autocor...

Bayesian compressed sensing with new sparsity-inducing prior

Sparse Bayesian learning (SBL) is a popular approach to sparse signal recovery in compressed sensing (CS). In SBL, the signal sparsity information is exploited by assuming a sparsity-inducing prior for the signal that is then estimated using Bayesian inference. In this paper, a new sparsity-inducing prior is introduced and efficient algorithms are developed for signal recovery. The main algorit...

Nonparametric prior for adaptive sparsity

For high-dimensional problems various parametric priors have been proposed to promote sparse solutions. While parametric priors has shown considerable success they are not very robust in adapting to varying degrees of sparsity. In this work we propose a discrete mixture prior which is partially nonparametric. The right structure for the prior and the amount of sparsity is estimated directly fro...

Extending CRAFT Data-Distributions for Sparse Matrices

Information Covariance Matrices for Multivariate Burr III and Logistic Distributions

Main result of this paper is to derive the exact analytical expressions of information and covariance matrices for multivariate Burr III and logistic distributions. These distributions arise as tractable parametric models in price and income distributions, reliability, economics, Human population, some biological organisms to model agricultural population data and survival data. We showed that ...