arXiv subjects

Zhiying Qiu

Publications and source records attributed to Zhiying Qiu.

3 recordsLinked to original sources

arXiv2026

A Graphical Framework for Testing Hierarchically Structured Hypothesis Families

In clinical trials, hypotheses are frequently organized into hierarchically ordered families, requiring specialized testing strategies that account for these structured relationships. Existing gatekeeping methods-including serial, parallel, and tree-structured approaches-provide important solutions but are often either too rigid or insufficiently intuitive to accommodate increasingly complex logical dependencies among hypothesis families. To address these limitations, we propose a novel family-based graphical approach that unifies the derivation and visualization of diverse gatekeeping strategies. In this framework, procedures are represented as directed, weighted graphs, where nodes correspond to hypothesis families. Two simple updating rules govern the allocation of significance levels within families and the propagation of significance levels between them. We establish that the proposed method strongly controls the familywise error rate (FWER) at a pre-specified level. Simulation studies under representative configurations indicate that the proposed procedure achieves performance comparable to hypothesis-level graphical approaches and competitive with the superchain procedure, while providing a simpler and more interpretable family-level representation. Case studies and a real clinical trial application further illustrate its flexibility and practical advantages, making it a powerful tool for managing hierarchically structured multiple testing in clinical research.

stat.ME↗

arXiv2025

Fast and Stable Diffusion Planning through Variational Adaptive Weighting

Diffusion models have recently shown promise in offline RL. However, these methods often suffer from high training costs and slow convergence, particularly when using transformer-based denoising backbones. While several optimization strategies have been proposed -- such as modified noise schedules, auxiliary prediction targets, and adaptive loss weighting -- challenges remain in achieving stable and efficient training. In particular, existing loss weighting functions typically rely on neural network approximators, which can be ineffective in early training phases due to limited generalization capacity of MLPs when exposed to sparse feedback in the early training stages. In this work, we derive a variationally optimal uncertainty-aware weighting function and introduce a closed-form polynomial approximation method for its online estimation under the flow-based generative modeling framework. We integrate our method into a diffusion planning pipeline and evaluate it on standard offline RL benchmarks. Experimental results on Maze2D and Kitchen tasks show that our method achieves competitive performance with up to 10 times fewer training steps, highlighting its practical effectiveness.

cs.LG↗

arXiv2016

Bonferroni-based gatekeeping procedure with retesting option

In complex clinical trials, multiple research objectives are often grouped into sets of objectives based on their inherent hierarchical relationships. Consequently, the hypotheses formulated to address these objectives are grouped into ordered families of hypotheses and thus to be tested in a pre-defined sequence. In this paper, we introduce a novel Bonferroni based multiple testing procedure for testing hierarchically ordered families of hypotheses. The proposed procedure allows the families to be sequentially tested more than once with updated local critical values. It is proved to control the global familywise error rate strongly under arbitrary dependence. Implementation of the procedure is illustrated using two examples. Finally, the procedure is extended to testing multiple families of hypotheses with a complex two-layer hierarchical structure.

stat.ME↗