7 citations · 8 across the 7 of their papers we have counts for
10 papers
Bayesian Multiplicity Correction in the Probabilistic Forward Stepwise Framework
Andrew Womack, Daniel Taylor-Rodriguez
We develop a natural Bayesian multiplicity-correcting prior distribution within the probabilistic forward stepwise representation of model space priors for regression problems. The…
Clustering the Nearest Neighbor Gaussian Process
Ashlynn Crisp, Daniel Taylor-Rodriguez, Andrew O. Finley
Gaussian processes are ubiquitous as the primary tool for modeling spatial data. However, the Gaussian process is limited by its cost, making direct parameter fi…
Identifiability of the Multinomial Processing Tree-IRT model for the Philadelphia Naming Test
Andrew J. Womack, Daniel Taylor-Rodriguez, Gerasimos Fergadiotis +1
Naming tests represent an essential tool in gauging the severity of aphasia and monitoring the trajectory of recovery for individuals afflicted with this debilitating condition. In…
Unstructured Primary Outcome in Randomized Controlled Trials
Daniel Taylor-Rodriguez, David Lovitz, Nora Mattek +4
The primary outcome of Randomized clinical Trials (RCTs) are typically dichotomous, continuous, multivariate continuous, or time-to-event. However, what if this outcome is unstruct…
On the Uniformity of Modulo 1
Paula Neeley, Daniel Taylor-Rodriguez, J. J. P. Veerman +1
It has been conjectured that the sequence modulo is uniformly distributed. The distribution of this sequence is signifcant in relation to unsolved problems in number…
Spatial Factor Models for High-Dimensional and Large Spatial Data: An Application in Forest Variable Mapping
Daniel Taylor-Rodriguez, Andrew O. Finley, Abhirup Datta +5
Gathering information about forest variables is an expensive and arduous activity. As such, directly collecting the data required to produce high-resolution maps over large spatial…
The matryoshka doll prior: principled multiplicity correction in Bayesian model comparison
Andrew J Womack, Daniel Taylor-Rodriguez, Claudio Fuentes
This paper introduces a general and principled construction of model space priors with a focus on regression problems. The proposed formulation regards each model as a `local` null…
On the estimation of the order of smoothness of the regression function
Daniel Taylor-Rodriguez, Sujit Ghosh
The order of smoothness chosen in nonparametric estimation problems is critical. This choice balances the tradeoff between model parsimony and data overfitting. The most common app…
Intrinsic Bayesian Analysis for Occupancy Models
Daniel Taylor-Rodriguez, Andrew Womack, Claudio Fuentes +1
Occupancy models are typically used to determine the probability of a species being present at a given site while accounting for imperfect detection. The survey data underlying the…
Bayesian Variable Selection on Model Spaces Constrained by Heredity Conditions
Daniel Taylor-Rodriguez, Andrew Womack, Nikolay Bliznyuk
This paper investigates Bayesian variable selection when there is a hierarchical dependence structure on the inclusion of predictors in the model. In particular, we study the type…