4 citations · 5 across the 7 of their papers we have counts for
7 papers
Revisiting the Behrens-Fisher Problem: Validity-First Optimality
Xiao Wang, Chuanhai Liu
The Behrens--Fisher problem concerns inference on the difference of two normal means when both variances are unknown and unequal. It is a classical example in which nuisance parame…
Elucidating Inferential Models with the Cauchy Distribution
Chuanhai Liu, Ryan Martin
Statistical inference as a formal scientific method to covert experience to knowledge has proven to be elusively difficult. While frequentist and Bayesian methodologies have been a…
Partial Conditioning for Inference of Many-Normal-Means with Hölder Constraints
Jiasen Yang, Xiao Wang, Chuanhai Liu
Inferential models have been proposed for valid and efficient prior-free probabilistic inference. As it gradually gained popularity, this theory is subject to further developments…
Circularly Symmetric Tests of Goodness-of-Fit
Chuanhai Liu
It is realized that existing powerful tests of goodness-of-fit are all based on sorted uniforms and, consequently, can suffer from the confounded effect of different locations and…
Reweighted and Circularized Anderson-Darling Tests of Goodness-of-Fit
Chuanhai Liu
This paper takes a look at omnibus tests of goodness of fit in the context of reweighted Anderson-Darling tests and makes threefold contributions. The first contribution is to prov…
Estimation of High-Dimensional Normal Means through Inferential Models
Samuel J. Eschker, Chuanhai Liu
The classical multivariate normal means problem remains conceptually unresolved. While shrinkage and empirical Bayes methods improve risk by imposing external geometric or hierarch…
Estimation of Over-parameterized Models from an Auto-Modeling Perspective
Yiran Jiang, Chuanhai Liu
From a model-building perspective, we propose a paradigm shift for fitting over-parameterized models. Philosophically, the mindset is to fit models to future observations rather th…