Mincen Liu | Searcharxiv

arXiv2026

Model-Based Co-Clustering for Censored Data

Co-clustering jointly partitions the rows and columns of a data matrix. Standard co-clustering methods for continuous data typically assume that entries are observed exactly. Matrices with censored entries, such as assay measurements below a detection limit, are therefore commonly completed by substitution or imputation before co-clustering. Since the completed values are treated as exact, the estimated block means and partitions depend on the completion rule. We propose the censored Gaussian latent block model (CGLBM), in which each censored entry contributes the probability of its reported interval. We establish identifiability under a known Type I censoring design. Approximate standard errors for the block means follow from the Fisher information under censoring. CGLBM is fitted by a generalized expectation--maximization algorithm, and its row and column class numbers are selected by a BIC approximation to the integrated classification likelihood. In simulation studies, boundary substitution and truncated normal imputation attenuate the contrasts between block means, whereas the partition recovery of multiple imputation drops the most under heavy censoring. CGLBM preserves the contrasts, and its partition recovery drops the least. The method is implemented in the R package CGLBM.

stat.ME↗

arXiv2025

Assessing Bias and Precision in State Policy Evaluations: A Comparative Analysis of Time-Varying Estimators Using Policy Simulations

Using state-level opioid overdose mortality data from 1999-2016, we simulated four time-varying treatment scenarios, which correspond to real-world policy dynamics (ramp up, ramp down, temporary and inconsistent). We then evaluated seven commonly used policy evaluation methods: two-way fixed effects event study, debiased autoregressive model, augmented synthetic control, difference-in-differences with staggered adoption, event study with heterogeneous treatment, two-stage differences-in-differences and differences-in-differences imputation. Statistical performance was assessed by comparing bias, standard errors, coverage, and root mean squared error over 1,000 simulations. Results Our findings indicate that estimator performance varied across policy scenarios. In settings where policy effectiveness diminished over time, synthetic control methods recovered effects with lower bias and higher variance. Difference-in-difference approaches, while offering reasonable coverage under some scenarios, struggled when effects were non-monotonic. Autoregressive methods, although demonstrating lower variability, underestimated uncertainty. Overall, a clear bias-variance tradeoff emerged, underscoring that no single method uniformly excelled across scenarios. Conclusions This study highlights the importance of tailoring the choice of estimator to the expected trajectory of policy effects. In dynamic time-varying settings, particularly when a policy has an anticipated diminishing impact, methods like augmented synthetic controls may offer advantages despite reduced precision. Researchers should carefully consider these tradeoffs to ensure robust and credible state-policy evaluations.

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