Priors and Posterior Computation in Linear Endogenous Variable Models with Imperfect Instruments

Shrinkage Priors for Linear Instrumental Variable Models with Many Instruments

This paper addresses the weak instruments problem in linear instrumental variable models from a Bayesian perspective. The new approach has two components. First, a novel predictor-dependent shrinkage prior is developed for the many instruments setting. The prior is constructed based on a factor model decomposition of the matrix of observed instruments, allowing many instruments to be incorporat...

Power-Expected-Posterior Priors for Variable Selection in Gaussian Linear Models

Imaginary training samples are often used in Bayesian statistics to develop prior distributions, with appealing interpretations, for use in model comparison. Expected-posterior priors are defined via imaginary training samples coming from a common underlying predictive distribution m, using an initial baseline prior distribution. These priors can have subjective and also default Bayesian implem...

Extended Linear Models with Gaussian Priors Extended Linear Models with Gaussian Priors

Posterior convergence rates in non-linear latent variable models

Non-linear latent variable models have become increasingly popular in a variety of applications. However, there has been little study on theoretical properties of these models. In this article, we study rates of posterior contraction in univariate density estimation for a class of non-linear latent variable models where unobserved U(0, 1) latent variables are related to the response variables v...

comparison of blood flow velocity changes in posterior cerebral artery measured with transcranial doppler sonography in migraineurs with visual aura and normal persons after photostimulation

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