arXiv subjects

Yizhou Cai

Publications and source records attributed to Yizhou Cai.

3 recordsLinked to original sources

arXiv2025

Stratified Bootstrap Test Package

The Stratified Bootstrap Test (SBT) provides a nonparametric, resampling-based framework for assessing the stability of group-specific ranking patterns in multivariate survey or rating data. By repeatedly resampling observations and examining whether a group's top-ranked items remain among the highest-scoring categories across bootstrap samples, SBT quantifies ranking robustness through a non-containment index. In parallel, the stratified bootstrap test extends this framework to formal statistical inference by testing ordering hypotheses among population means. Through resampling within groups, the method approximates the null distribution of ranking-based test statistics without relying on distributional assumptions. Together, these techniques enable both descriptive and inferential evaluation of ranking consistency, detection of aberrant or adversarial response patterns, and rigorous comparison of groups in applications such as survey analysis, item response assessment, and fairness auditing in AI systems.

stat.ME↗

arXiv2024

Maximum Ideal Likelihood Estimation: A Unified Inference Framework for Latent Variable Models

This paper develops a unified estimation framework, the Maximum Ideal Likelihood Estimation (MILE), for general parametric models with latent variables. Unlike traditional approaches relying on the marginal likelihood of the observed data, MILE directly exploits the joint distribution of the complete data by treating the latent variables as parameters (the ideal likelihood). Borrowing strength from optimisation techniques and algorithms, MILE is a broadly applicable framework in case that traditional methods fail, such as when the marginal likelihood has non-finite expectations. MILE offers a flexible and robust alternative to established techniques, including the Expectation-Maximisation algorithm and Markov chain Monte Carlo. We facilitate statistical inference of MILE on consistency, asymptotic distribution, and equivalence to the Maximum Likelihood Estimation, under some mild conditions. Extensive simulations illustrative real-data applications illustrate the empirical advantages of MILE, outperforming existing methods on computational feasibility and scalability.

math.ST↗

arXiv2024

Testing Complex Singlet Scalar Cosmology at the Large Hadron Collider

The Standard Model extended with a complex singlet scalar (cxSM) can admit a strong first order electroweak phase transition (SFOEWPT) as needed for electroweak baryogenesis and provide a dark matter (DM) candidate. The presence of both a DM candidate and a singlet-like scalar that mixes with the Standard Model Higgs boson leads to the possibility of a $b\bar{b}+\text{MET}$ final state in $pp$ collisions. Focusing on this channel, we analyze the prospective reach at the Large Hadron Collider (LHC) for a heavy singlet-like scalar in regions of cxSM parameter space compatible with a SFOEWT and DM phenomenology. We identify this parameter space while implementing current constraints from electroweak precision observable and Higgs boson property measurements as well as those implied by LHC heavy resonance searches.

hep-ph↗