Exact Markov Chain Monte Carlo with Likelihood Approximations for Functional Linear Models
Author | : Corey James Smith |
Publisher | : |
Total Pages | : |
Release | : 2018 |
Genre | : Bayesian statistical decision theory |
ISBN | : |
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Functional data analysis is a branch of statistics that deals with the theory and analysis of data which may be comprised of functions in addition to scalar values. Here we consider the linear model that relates functional covariates to scalar responses. We introduce an exact MCMC algorithm which does not rely on likelihood evaluations to estimate the parameter function. The proposed method uses Barker's algorithm (as opposed to Metropolis-Hastings). Though Barker's has been shown to be asymptotically less efficient than Metropolis-Hastings, the form of its acceptance probability allows us to make the accept/reject decision efficiently without needing to evaluate the likelihood function. We utilize unbiased estimates of the log-likelihood function along with two nested Bernoulli factories to accomplish this. In addition, exact MCMC methods for logistic and Poisson regression settings with functional predictors are provided. These latter two models again feature Bernoulli factories and Barker's algorithm while also making use of debiasing techniques to aid in log-likelihood estimation.