Jialiang Li

Evaluating diagnostic accuracy for multi‐category outcomes remains a significant challenge, primarily due to computational limitations in existing performance metrics. The Polytomous Discrimination Index (PDI) has emerged as an order‐agnostic solution suitable for nominal classifications. However, its broader adoption has been constrained by the lack of efficient implementation, especially for its variance estimation, which typically would require computationally intensive bootstrapping procedures. In this work, we address this limitation by proposing a novel asymptotic variance estimator for the PDI. Our method integrates classical ‐statistic theory with recent advances in combinatorics, offering a scalable and theoretically grounded alternative. To assess the performance of the proposed approach, we conduct extensive simulation studies and observe remarkable gain in computing time. We further apply our method to a real‐world brain image analysis where deep neural networks are used as a diagnostic tool. We can efficiently report the accuracy of the neural networks with different depth specifications.

Individualized treatment regimes (ITRs) represent decision-making frameworks that tailor treatment assignments to individual patient characteristics. The value function of an ITR quantifies the expected outcome under a counterfactual scenario in which such a treatment rule is applied. However, estimating optimal ITRs for survival data remains a significant challenge when outcomes are right-censored and only a subset of patients has complete outcome information due to time and cost constraints. To overcome this challenge, we formulate the problem within a semi-supervised learning framework and adopt an induced missingness perspective to model partially observed survival outcomes. We propose an imputation-based semi-supervised approach that is robust and adaptable to various imputation models. Specifically, we employ a flexible single-index kernel smoothing imputation technique to effectively utilize unlabeled data in multidimensional covariate settings. The proposed estimators for the parameters indexing the optimal ITRs are shown to be consistent and asymptotically normal. Moreover, semi-supervised estimation enhances efficiency by reducing asymptotic variance relative to supervised estimation. Numerical experiments on both simulated and real datasets demonstrate the superior performance of our proposed semi-supervised approach.

Subgroup identification is a significant research area in statistics and machine learning, aiming to partition a heterogeneous population into more homogeneous subgroups to enable precise inference and personalized decision‐making. Among the various tools available, change point analysis has emerged as a powerful approach for detecting structural changes in data sequences, and it plays an increasingly important role in subgroup identification. In this paper, we provide a systematic review of recent advances in subgroup identification methods based on efficient multiple change point detection methods: (a) We first review the two‐step multiple change point detection method (TSMCD) and its application from linear regression to survival analysis. (b) We then discuss the construction of the threshold variable, including the recent developments in the change plane regression model and the change surface regression model. This review aims to provide researchers with a comprehensive perspective to promote the further application and development of change point analysis in subgroup identification and precision medicine.

Motivated by recent development of tensor regression modeling, we propose a novel tensor ensemble learning (TEL) approach. While CANDECOMP/PARAFAC (CP) decomposition is an efficient technique to reduce the number of parameters in tensor covariate, determining an appropriate CP rank remains uncertain in practice. Furthermore, the structural complexity of tensor often varies across different spatial regions. To explore the intrinsic tensor structure, we design different tensor partition strategies and divide tensor into disjoint blocks to form candidate models. A model ensemble method is then developed to explore the uncertainty in both tensor block structure and CP rank. To assign weights to candidate models and enhance predictive performance, we leverage the predictability, computability, and stability framework for veridical data science. Simulation studies are carried out to assess the performance of TEL under varying levels of tensor complexity. We further apply TEL to two real data applications about glaucoma management using fundus image and cognitive ability prediction in Alzheimer’s disease using neuroimaging. Our numerical studies illustrate the superiority of TEL over existing methods and highlight its promise for complex tensor data analysis.

The community detection problem on multilayer networks have drawn much interest. When the nodal covariates ar also present, few work has been done to integrate information from both sources. To leverage the multilayer networks and the covariates, we propose two new algorithms: the spectral clustering on aggregated networks with covariates (SCANC), and the spectral clustering on aggregated Laplacian with covariates (SCALC). These two algorithms are easy to implement, computationally fast, and feature a data-driven approach for tuning parameter selection. We establish theoretical guarantees for both methods under the Multilayer Stochastic Blockmodel with Covariates (MSBM-C), demonstrating their consistency in recovering community structure. Our analysis reveals that increasing the number of layers, incorporating covariate information, and enhancing network density all contribute to improved clustering accuracy. Notably, SCANC is most effective when all layers exhibit similar assortativity, whereas SCALC performs better when both assortative and disassortative layers are present. On the simulation studies and a primary school contact data analysis, our method outperforms other methods. Our results highlight the advantages of spectral-based aggregation techniques in leveraging both network structure and nodal attributes for robust community detection.