8 citations · 24 across the 9 of their papers we have counts for
10 papers
Self-Tuned Rejection Sampling within Gibbs and a Case Study in Small Area Estimation
Andrew M. Raim, Kyle M. Irimata, James A. Livsey
When formulating a Gibbs sampler, some conditionals may be unfamiliar distributions without well-known variate generation routines. Rejection sampling may be used to draw from such…
Bayesian Methods to Improve The Accuracy of Differentially Private Measurements of Constrained Parameters
Ryan Janicki, Scott H. Holan, Kyle M. Irimata +2
Formal disclosure avoidance techniques are necessary to ensure that published data can not be used to identify information about individuals. The addition of statistical noise to u…
Rejection Sampling with Vertical Weighted Strips
Andrew M. Raim, James A. Livsey, Kyle M. Irimata
A number of distributions that arise in statistical applications can be expressed in the form of a weighted density: the product of a base density and a nonnegative weight function…
Direct Sampling with a Step Function
Andrew M. Raim
The direct sampling method proposed by Walker et al. (JCGS 2011) can generate draws from weighted distributions possibly having intractable normalizing constants. The method may be…
Bayesian Nonparametric Multivariate Spatial Mixture Mixed Effects Models with Application to American Community Survey Special Tabulations
Ryan Janicki, Andrew M. Raim, Scott H. Holan +1
Leveraging multivariate spatial dependence to improve the precision of estimates using American Community Survey data and other sample survey data has been a topic of recent intere…
A Conway-Maxwell-Multinomial Distribution for Flexible Modeling of Clustered Categorical Data
Darcy Steeg Morris, Andrew M. Raim, Kimberly F. Sellers
Categorical data are often observed as counts resulting from a fixed number of trials in which each trial consists of making one selection from a prespecified set of categories. Th…
Spatio-Temporal Change of Support Modeling with R
Andrew M. Raim, Scott H. Holan, Jonathan R. Bradley +1
Spatio-temporal change of support methods are designed for statistical analysis on spatial and temporal domains which can differ from those of the observed data. Previous work intr…
Parallelizing Computation of Expected Values in Recombinant Binomial Trees
Sai K. Popuri, Andrew M. Raim, Nagaraj K. Neerchal +1
Recombinant binomial trees are binary trees where each non-leaf node has two child nodes, but adjacent parents share a common child node. Such trees arise in finance when pricing a…
ManifoldOptim: An R Interface to the ROPTLIB Library for Riemannian Manifold Optimization
Sean Martin, Andrew M. Raim, Wen Huang +1
Manifold optimization appears in a wide variety of computational problems in the applied sciences. In recent statistical methodologies such as sufficient dimension reduction and re…
An Extension of Generalized Linear Models to Finite Mixture Outcome Distributions
Andrew M. Raim, Nagaraj K. Neerchal, Jorge G. Morel
Finite mixture distributions arise in sampling a heterogeneous population. Data drawn from such a population will exhibit extra variability relative to any single subpopulation. St…