arXiv subjects

Sijie Zheng

Publications and source records attributed to Sijie Zheng.

4 recordsLinked to original sources

arXiv2026

Debiased Machine Learning for Partially Linear Accelerated Failure Time Models

The Cox model remains the default for survival analysis, but the proportional hazards assumption is often violated and hazard ratios can be difficult to interpret. Accelerated failure time (AFT) models provide an intuitive time-scale alternative, yet flexible covariate adjustment while preserving valid inference on a target exposure remains challenging. For the partially linear AFT model under right censoring, a rank-based debiased machine learning (DML) framework remains undeveloped: the rank-based pairwise moment is not Neyman orthogonal and standard cross-fitting does not directly apply to U-statistics. We develop the first such framework by combining an orthogonalized rank-based U-statistic, a censoring-corrected influence function, and block-pairwise cross-fitting, yielding valid inference under flexible nuisance estimation. Simulations and an application to All of Us electronic health record data demonstrate finite-sample performance and practical utility.

stat.ME↗

arXiv2026

A Machine Learning--Based Surrogate EKMA Framework for Diagnosing Urban Ozone Formation Regimes: Evidence from Los Angeles

Surface ozone pollution remains a persistent challenge in many metropolitan regions worldwide, as the nonlinear dependence of ozone formation on nitrogen oxides and volatile organic compounds (VOCs) complicates the design of effective emission control strategies. While chemical transport models provide mechanistic insights, they rely on detailed emission inventories and are computationally expensive. This study develops a machine learning--based surrogate framework inspired by the Empirical Kinetic Modeling Approach (EKMA). Using hourly air quality observations from Los Angeles during 2024--2025, a random forest model is trained to predict surface ozone concentrations based on precursor measurements and spatiotemporal features, including site location and cyclic time encodings. The model achieves strong predictive performance, with permutation importance highlighting the dominant roles of diurnal temporal features and nitrogen dioxide, along with additional contributions from carbon monoxide. Building on the trained surrogate, EKMA-style sensitivity experiments are conducted by perturbing precursor concentrations while holding other covariates fixed. The results indicate that ozone formation in Los Angeles during the study period is predominantly VOC-limited. Overall, the proposed framework offers an efficient and interpretable approach for ozone regime diagnosis in data-rich urban environments.

stat.AP↗

arXiv2025

Estimation of Semiparametric Factor Models with Missing Data

We study semiparametric factor models in high-dimensional panels where the factor loadings consist of a nonparametric component explained by observed covariates and an idiosyncratic component capturing unobserved heterogeneity. A key challenge in empirical applications is the presence of missing observations, which can distort both factor recovery and loading estimation. To address this issue, we develop a projected principal component analysis (PPCA) procedure that accommodates general missing-at-random mechanisms through inverse-probability weighting. We establish consistency and derive the asymptotic distributions of the estimated factors and loading functions, allowing the sieve dimension to diverge and permitting the time dimension to be either fixed or growing. Unlike classical PCA, PPCA achieves consistent factor estimation even when T is fixed, and the limiting distributions under missing data exhibit mixture normality with enlarged asymptotic variances. Theoretical results are supported by simulations and an empirical application. Our findings demonstrate that PPCA provides an effective and robust framework for estimating semiparametric factor models in the presence of missing data.

stat.ME↗

arXiv2025

Efficient Covariance Estimation for Sparsified Functional Data

Motivated by recent work involving the analysis of leveraging spatial correlations in sparsified mean estimation, we present a novel procedure for constructing covariance estimator. The proposed Random-knots (Random-knots-Spatial) and B-spline (Bspline-Spatial) estimators of the covariance function are computationally efficient. Asymptotic pointwise of the covariance are obtained for sparsified individual trajectories under some regularity conditions. Our proposed nonparametric method well perform the functional principal components analysis for the case of sparsified data, where the number of repeated measurements available per subject is small. In contrast, classical functional data analysis requires a large number of regularly spaced measurements per subject. Model selection techniques, such as the Akaike information criterion, are used to choose the model dimension corresponding to the number of eigenfunctions in the model. Theoretical results are illustrated with Monte Carlo simulation experiments. Finally, we cluster multi-domain data by replacing the covariance function with our proposed covariance estimator during PCA.

stat.ME↗