Iterative Likelihood: A Unified Inference Tool
We propose a framework for inference based on an “iterative likelihood function,” which provides a unified representation for a number of iterative approaches, including the EM algorithm and th…
Optimal Sampling for Generalized Linear Models Under Measurement Constraints
Under “measurement constraints,” responses are expensive to measure and initially unavailable on most of records in the dataset, but the covariates are available for the entire dataset. Our goa…
Density Deconvolution With Additive Measurement Errors Using Quadratic Progra…
Distribution estimation for noisy data via density deconvolution is a notoriously difficult problem, especially for typical noise distributions like Gaussian. We develop a density deconvolution est…
Additive Function-on-Function Regression
We study additive function-on-function regression where the mean response at a particular time point depends on the time point itself, as well as the entire covariate trajectory. We develop a compu…
Local Derivative-Free Approximation of Computationally Expensive Posterior De…
Bayesian inference using Markov chain Monte Carlo (MCMC) is computationally prohibitive when the posterior density of interest, π, is computationally expensive to evaluate. We develop a derivative…
Tapered Covariance: Bayesian Estimation and Asymptotics
The method of maximum tapered likelihood has been proposed as a way to quickly estimate covariance parameters for stationary Gaussian random fields. We show that under a useful asymptotic regime, m…
RAPTT: An Exact Two-Sample Test in High Dimensions Using Random Projections
In high dimensions, the classical Hotelling’s T2 test tends to have low power or becomes undefined due to singularity of the sample covariance matrix. In this article, this problem is overcome by…
Efficient Interpolation of Computationally Expensive Posterior Densities With…
Markov chain Monte Carlo (MCMC) is nowadays a standard approach to numerical computation of integrals of the posterior density π of the parameter vector η. Unfortunately, Bayesian inference using…
Spatially Adaptive Bayesian Penalized Splines With Heteroscedastic Errors
Penalized splines have become an increasingly popular tool for nonparametric smoothing because of their use of low-rank spline bases, which makes computations tractable while maintaining accuracy a…
Bayesian Calibration and Uncertainty Analysis for Computationally Expensive M…
We presenta Bayesian approach to model calibration when evaluation of the model is computationally expensive. Here, calibration is a nonlinear regression problem: given a data vector Y correspondin…
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