This monograph develops probability and stochastic-process ideas as a translation language for statistics: from designed observations and data objects to targets, stability statements, inference, and use. The chapters move from motivating examples and randomization through probability measures, kernels, likelihoods, data objects, weak convergence, empirical fields, functional data, M- and Z-estimation, testing, local approximations, event-time processes, and prediction. Historical and biomedical examples are used to keep abstract objects tied to records, mechanisms, and decisions. The aim is to give readers a common grammar for classical probability, modern data structures, and statistical practice.

翻译：本专著将概率与随机过程思想发展为统计学的翻译语言：从设计性观测与数据对象到目标、稳定性陈述、推断及实际应用。各章节从动机示例与随机化出发，依次涵盖概率测度、核函数、似然函数、数据对象、弱收敛、经验场、函数型数据、M估计与Z估计、检验、局部逼近、事件时间过程及预测。通过历史与生物医学案例，将抽象对象始终锚定于记录、机制与决策。旨在为读者构建一套贯通经典概率论、现代数据结构及统计实践的通用语法体系。