1 citations · 1 across the 6 of their papers we have counts for
6 papers
A Case-Study of Sample-Based Bayesian Forecasting Algorithms
Taylor R. Brown
For a Bayesian, real-time forecasting with the posterior predictive distribution can be challenging for a variety of time series models. First, estimating the parameters of a time…
The Most Difference in Means: A Statistic for the Strength of Null and Near-Zero Results
Bruce A. Corliss, Taylor R. Brown, Tingting Zhang +3
Statistical insignificance does not suggest the absence of effect, yet scientists must often use null results as evidence of negligible (near-zero) effect size to falsify scientifi…
Approximating Posterior Predictive Distributions by Averaging Output From Many Particle Filters
Taylor R. Brown
This paper introduces the {\it particle swarm filter} (not to be confused with particle swarm optimization): a recursive and embarrassingly parallel algorithm that targets an appro…
PF: A C++ Library for Fast Particle Filtering
Taylor R. Brown
Particle filters are a class of algorithms that are used for "tracking" or "filtering" in real-time for a wide array of time series models. Despite their comprehensive applicabilit…
A Pseudo-Marginal Metropolis-Hastings Algorithm for Estimating Generalized Linear Models in the Presence of Missing Data
Taylor R. Brown, Timothy L. McMurry, Alexander Langevin
The missing data issue often complicates the task of estimating generalized linear models (GLMs). We describe why the pseudo-marginal Metropolis-Hastings algorithm, used in this se…
A Factor Stochastic Volatility Model with Markov-Switching Panic Regimes
Taylor R. Brown
The use of factor stochastic volatility models requires choosing the number of latent factors used to describe the dynamics of the financial returns process; however, empirical evi…