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分层空间上的非参数统计及其在对象数据分析中的应用
Victor Patrangenaru and Daniel E. Osborne. Boca Raton, FL: Chapman & Hall/CRC Press, 2025, xv + 187 pp., $189.99(H), ISBN: 978-1-138-04313-8.Nowadays, more and more data we meet are not just simple...
离散观测函数集合的常微分方程模型
Abstract The exploration of dynamic systems governed by ordinary differential equations (ODEs) holds great interest in the field of statistics. Existing research mainly focuses on a single function. This study generalizes the scope to analyse a collection of functions observed at discretized times, with sampling frequencies varying from sparse to dense designs. The range of ODE models studied caters to diverse dynamic systems, and includes the complex nonlinear and…
超越总体变量重要性：个体变量重要性的概念、理论与应用
Abstract In a non-parametric regression setting, we introduce a novel concept of ‘individual variable importance’, which assesses the relevance of certain covariates to an outcome variable among individuals with specific characteristics. This concept holds practical importance for both risk assessment and association identification. For example, it can represent (i) the usefulness of expensive biomarkers in risk prediction for individuals at a specified baseline risk, or…
稀疏纵向观测的内蕴黎曼函数型数据分析
A new framework is developed to intrinsically analyze sparsely observed Riemannian functional data. It features four innovative components: a frame-independent covariance function, a smooth vector bundle termed covariance vector bundle, a parallel transport and a smooth bundle metric on the covariance vector bundle. The introduced intrinsic covariance function links estimation of covariance structure to smoothing problems that involve raw covariance observations derived from…