24 citations · 28 across the 6 of their papers we have counts for
9 papers · 1 filter
Bayesian Inference on Volatility in the Presence of Infinite Jump Activity and Microstructure Noise
Qi Wang, José E. Figueroa-López, Todd Kuffner
Volatility estimation based on high-frequency data is key to accurately measure and control the risk of financial assets. A Lévy process with infinite jump activity and microstruct…
Block bootstrap optimality for density estimation with dependent data
Todd A. Kuffner, Stephen M. -S. Lee, G. Alastair Young
Accurate approximation of the sampling distribution of nonparametric kernel density estimators is crucial for many statistical inference problems. Since these estimators have compl…
On overfitting and post-selection uncertainty assessments
Liang Hong, Todd A. Kuffner, Ryan Martin
In a regression context, when the relevant subset of explanatory variables is uncertain, it is common to use a data-driven model selection procedure. Classical linear model theory,…
Optimal hybrid block bootstrap for sample quantiles under weak dependence
Todd A. Kuffner, Stephen M. S. Lee, G. Alastair Young
We establish a general theory of optimality for block bootstrap distribution estimation for sample quantiles under a mild strong mixing assumption. In contrast to existing results,…
On the validity of the formal Edgeworth expansion for posterior densities
John E. Kolassa, Todd A. Kuffner
We consider a fundamental open problem in parametric Bayesian theory, namely the validity of the formal Edgeworth expansion of the posterior density. While the study of valid asymp…
Bayes factor consistency
Siddhartha Chib, Todd A. Kuffner
Good large sample performance is typically a minimum requirement of any model selection criterion. This article focuses on the consistency property of the Bayes factor, a commonly…