为什么有的人群能够拥有更长的健康预期寿命？不同出生队列、社会经济群体或地区之间健康预期寿命（Health Expectancy）的差异究竟来源于哪些因素，又集中发生在哪些年龄阶段？长期以来，健康预期寿命已成为衡量健康老龄化的重要指标，但现有研究多侧重于描述差异的大小，难以进一步揭示其形成机制。针对这一问题，中国人民大学统计学院王晓军教授团队提出了一种基于纵向随访数据的健康预期寿命归因—分解新方法，实现了从年龄和影响因素两个维度解析不同人群健康预期寿命差异的来源。与传统基于横断面数据的方法相比，该方法能够利用个体长期随访信息，更准确地刻画健康变化和死亡过程，并进一步量化不同影响因素在不同年龄阶段对健康预期寿命差异的具体贡献。该框架所考虑的影响因素不仅包括疾病和死因，也可以扩展至生活方式、社会经济条件、环境暴露以及遗传风险等多种因素。该研究不仅能够回答“健康预期寿命差异有多大”，更能够回答“健康预期寿命差异从哪里来”，为识别影响健康老龄化的关键因素和关键年龄窗口提供了新的统计工具。与此同时，团队开发了配套软件包 LongDecompHE 和可视化 Shiny 平台，方便研究人员开展健康预期寿命的测算、归因与分解分析。该研究为理解人口健康不平等、优化慢性病防控策略以及推进健康中国和积极应对人口老龄化国家战略提供了新的方法学支撑。相关成果以“Decomposing Differences in Cohort Health Expectancy by Cause and Age with Longitudinal Data”为题发表于人口学领域国际顶级期刊《Demography》（https://doi.org/10.1215/00703370-12654071）。

In spatial and social network analysis, multivariate spatial autoregressive (MSAR) models are effective tools for analyzing network data with multivariate responses. When the dimension of the response is divergent, however, the number of unknown parameters in an MSAR increases at a rate that is proportional to the square of the dimensionality, which poses significant challenges to the model estimation process. To address this issue, we propose a novel reduced-rank MSAR model by imposing a low-rank structure on the spatial influence matrix of the multivariate responses. The proposed model achieves substantial dimensionality reduction and offers insightful interpretations. To mitigate the high computational cost of the quasi-maximum likelihood estimator (QMLE), we propose a least squares estimator (LSE) for estimating the unknown parameters. Furthermore, we establish the asymptotic nature of the LSE when both the network size and the dimensionality of the responses diverge to infinity. To determine the rank, we propose an information criterion estimator and demonstrate the consistency of its rank selection process. Extensive numerical simulations validate the proposed model and parameter estimates. Finally, a dataset derived from Shouqianba, one of the largest aggregate payment platforms, is analyzed for illustration purposes.