arXiv subjects

Jiakun Jiang

Publications and source records attributed to Jiakun Jiang.

3 recordsLinked to original sources

arXiv2026

Unifying Data-Adaptive Longitudinal Treatment-Effect Subgroup Discovery and Pretreatment Membership Prediction

Precision medicine seeks to match patients with treatments from which they are most likely to benefit. A key step toward this goal is to learn treatment-effect heterogeneity from longitudinal study data. This requires discovering subgroups with distinct treatment-effect trajectories and predicting subgroup membership for new patients from pretreatment information. Existing methods do not fully unify these tasks. Covariate-informed membership models typically prespecify the subgroup number or covariance structure, whereas data-adaptive discovery methods often require prediction in a separate stage. We propose a data-adaptive semiparametric mixture-of-experts framework that unifies longitudinal treatment-effect subgroup discovery and pretreatment membership prediction. Subgroup-specific varying-coefficient functions characterize treatment-effect trajectories, while flexible covariance functions account for within-subject dependence. A covariate-dependent gating model links membership to pretreatment characteristics. A penalty on global component weights removes redundant subgroups, while separate gating coefficients allow patient-specific membership probabilities among retained subgroups. A blockwise generalized expectation--maximization algorithm estimates all model components under a single penalized observed-data objective. We establish consistent subgroup-number selection, convergence rates for the functional estimators, and asymptotic normality for the finite-dimensional parameters. Simulations show reliable subgroup-number selection and more accurate conditional membership prediction than a two-step procedure. Applications to the SANAD epilepsy study and the COMPETE migraine trial identify distinct temporal treatment-effect patterns.

stat.ME↗

arXiv2026

Sparse Convex Biclustering

Biclustering is an essential unsupervised machine learning technique for simultaneously clustering rows and columns of a data matrix, with widespread applications in genomics, transcriptomics, and other high-dimensional omics data. Despite its importance, existing biclustering methods struggle to meet the demands of modern large-scale datasets. The challenges stem from the accumulation of noise in high-dimensional features, the limitations of non-convex optimization formulations, and the computational complexity of identifying meaningful biclusters. These issues often result in reduced accuracy and stability as the size of the dataset increases. To overcome these challenges, we propose Sparse Convex Biclustering (SpaCoBi), a novel method that penalizes noise during the biclustering process to improve both accuracy and robustness. By adopting a convex optimization framework and introducing a stability-based tuning criterion, SpaCoBi achieves an optimal balance between cluster fidelity and sparsity. Comprehensive numerical studies, including simulations and an application to mouse olfactory bulb data, demonstrate that SpaCoBi significantly outperforms state-of-the-art methods in accuracy. These results highlight SpaCoBi as a robust and efficient solution for biclustering in high-dimensional and large-scale datasets.

stat.ML↗

arXiv2023

Dynamic online prediction model and its application to automobile claim frequency data

Prediction modelling of claim frequency is an important task for pricing and risk management in non-life insurance and needed to be updated frequently with the changes in the insured population, regulatory legislation and technology. Existing methods are either done in an ad hoc fashion, such as parametric model calibration, or less so for the purpose of prediction. In this paper, we develop a Dynamic Poisson state space (DPSS) model which can continuously update the parameters whenever new claim information becomes available. DPSS model allows for both time-varying and time-invariant coefficients. To account for smoothness trends of time-varying coefficients over time, smoothing splines are used to model time-varying coefficients. The smoothing parameters are objectively chosen by maximum likelihood. The model is updated using batch data accumulated at pre-specified time intervals, which allows for a better approximation of the underlying Poisson density function. The proposed method can be also extended to the distributional assumption of zero-inflated Poisson and negative binomial. In the simulation, we show that the new model has significantly higher prediction accuracy compared to existing methods. We apply this methodology to a real-world automobile insurance claim data set in China over a period of six years and demonstrate its superiority by comparing it with the results of competing models from the literature.

stat.AP↗