collaborators

6 papers

stat.ME2026

Identifiability of the instrumental variable model with the treatment and outcome missing not at random

Shuozhi Zuo, Peng Ding, Fan Yang

The instrumental variable model of Imbens and Angrist (1994) and Angrist et al. (1996) identifies the local average treatment effect, also known as the complier average causal effe…

q-fin.RM2026

Asymptotic Analysis of Optimal Diversification in Catastrophe Risk Pooling

Minh Chau Nguyen, Tony S. Wirjanto, Fan Yang

Catastrophe risk has long been recognized to pose a serious threat to the insurance sector. Catastrophe risk pooling offers an effective way to diversify losses arising from catast…

stat.ME2026

Self-separated and self-connected models for mediator and outcome missingness in mediation analysis

Trang Quynh Nguyen, Razieh Nabi, Fan Yang +2

Missing data is a common challenge in studying treatment effects. In the context of mediation analysis, this paper addresses missingness in the mediator and outcome, focusing on id…

math.ST2026

Stratified Permutational Berry--Esseen Bounds and Their Applications to Statistics

Pengfei Tian, Fan Yang, Peng Ding

The stratified linear permutation statistic arises in various statistics problems, including stratified and post-stratified survey sampling, stratified and post-stratified experime…

stat.ME2026

Identification and estimation of the conditional average treatment effect with nonignorable missing covariates, treatment, and outcome

Shuozhi Zuo, Yixin Wang, Fan Yang

Treatment effect heterogeneity is central to policy evaluation, social science, and precision medicine, where interventions can affect individuals differently. In observational stu…

stat.ME2026

Two-Phase Treatment with Noncompliance: Identifying the Cumulative Average Treatment Effect via Multisite Instrumental Variables

Guanglei Hong, Xu Qin, Zhengyan Xu +1

When evaluating a two-phase intervention, the cumulative average treatment effect (ATE) is often the primary causal estimand of interest. However, some individuals who do not respo…