2 citations · 4 across the 6 of their papers we have counts for
7 papers
Splitting strategies for post-selection inference
Daniel G. Rasines, G. Alastair Young
We consider the problem of providing valid inference for a selected parameter in a sparse regression setting. It is well known that classical regression tools can be unreliable in…
Bayesian Selective Inference: Non-informative Priors
Daniel G. Rasines, G. Alastair Young
We discuss Bayesian inference for parameters selected using the data. First, we provide a critical analysis of the existing positions in the literature regarding the correct Bayesi…
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…
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,…
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…