24 citations · 28 across the 6 of their papers we have counts for
7 papers
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…
Quantifying nuisance parameter effects via decompositions of asymptotic refinements for likelihood-based statistics
Thomas J. DiCiccio, Todd A. Kuffner, G. Alastair Young
Accurate inference on a scalar interest parameter in the presence of a nuisance parameter may be obtained using an adjusted version of the signed root likelihood ratio statistic, i…
Stability and uniqueness of -values for likelihood-based inference
Thomas J. DiCiccio, Todd A. Kuffner, G. Alastair Young +1
Likelihood-based methods of statistical inference provide a useful general methodology that is appealing, as a straightforward asymptotic theory can be applied for their implementa…