统计方法学
Reproducible and generalizable assessment of treatment decisions requires principled handling of subsequent treatment switching that may inform expected outcomes and shift across cohorts and over time. To effectively account for informative…
Scientific Abstract: In psychological network analysis, centrality indices are commonly used to evaluate the importance of nodes within a network. However, centrality only captures the static topological position of a node, and there is no…
We propose for a quantile regression an estimation method for transferring knowledge using two $L_1$ penalties based on an estimator obtained from a source database. The proposed transfer learning estimator satisfies the properties of…
There is increasing evidence of a directional relationship from academic performance to self-efficacy. We develop a Bayesian model for investigating this relationship when academic performance is measured on an ordinal scale and…
Predicting a response based on covariates is a fundamental problem in statistics and machine learning. However, profound difficulties arise when the underlying low-dimensional structural parameters are unidentifiable, as typified in…
Combined unilateral and bilateral binary outcomes frequently arise in studies involving paired organs. The risk difference is a clinically interpretable measure for comparing treatment effects between groups. Existing confidence interval…
Hybrid controlled trials (HCTs) augment randomized controlled trials (RCTs) with external controls (ECs) to improve statistical efficiency when RCTs face limited sample sizes, slow accrual, or ethical constraints. However, valid use of ECs…
In longitudinal studies, outcomes of interest are often truncated by death, meaning that they are only observed or well-defined conditional on intercurrent events such as survival. Existing strategies face a trade-off: causally…
We devise a novel estimator for a general quantile regression model with normal measurement errors in the covariates. The method is applicable to both linear and nonlinear quantile regressions and does not impose the quantile requirement on…
Prediction from sequential panel data is central to digital-twin modeling, where new panels arrive over time and the predictive system is updated sequentially. Existing methods often rely on temporal proximity, which can fail when similar…
Insurance payments may depend on latent micro states although only macro states and realized payments are observed. We study a sojourn-payment model for such aggregated multi-state systems under left-truncation and right-censoring. Starting…
We develop a Bayesian area-level small area estimation framework that jointly models binomial and Gaussian survey responses through shared spatial random effects. This work is motivated by the American Community Survey (ACS), which provides…
Identifying patients who are likely to benefit from a treatment is central to precision medicine and can guide follow-up trials, enrichment designs, and individualized decisions. Although randomized controlled trials (RCTs) provide evidence…
Nitrogen fertilizer management plays a central role in balancing agricultural productivity and environmental sustainability, yet identifying optimal application strategies remains difficult because treatment responses vary substantially…
The Gaussian Kernel Robust Regression method (GKRReg) is a robust regression estimator that iteratively re-weights observations via a Gaussian kernel so that outliers and leverage points receive near-zero weight, with convergence of the…
Functional principal component analysis (FPCA) is a central tool for dimension reduction and covariance analysis in functional data analysis. We study FPCA for discretely observed scalar-valued functional data indexed by a compact…
Demographic corrections are routinely performed in many disciplines, including psychology. Yet, there are ongoing debates about whether these corrections are appropriate and improve classification accuracy. Here, we focus on cognitive…
The increasing availability of diverse data sources has motivated great interest in data integration for improving regression efficiency. Existing data integration methods primarily focus on integrating nonprobability samples and typically…
Functional data analysis (FDA) provides statistical methods for analyzing samples of time-continuous stochastic processes. Measurements often arise in the form of sensor data for a key scientific variable. The practical problem of irregular…
Modern experiments, including evaluations of AI-enabled services and platform interventions, often depart from independent and identically distributed (i.i.d.) sampling because assignments may be adaptive, balanced across covariates, or…