AI 中文总结
本文提出一种新的回顾性统计推断范式，以观测数据的点估计为核心生成复制品分布，可用于非参数和参数回归，避免前瞻性有限维模型假设，为非参数函数估计提供替代不确定性量化方案。
AI 中文摘要
本文探讨一种新的统计推断范式，该方法以基于观测数据的点估计为核心，将该估计值作为真值模拟重复样本以生成估计值的复制品，推断结果来自复制品分布。它避免了前瞻性有限维模型假设，但不对真值做出概率断言；它提出了一种可用于非参数函数估计的替代不确定性量化系统。通过非参数回归中的平滑样条方差分析模型示例演示了该方法，该范式也适用于参数回归，其中所提出的推断在操作上与传统推断极为相似，全文散布着概念性讨论。
英文摘要
In this article, we explore a new paradigm for statistical inference. The approach centers around the point estimate based on observed data, simulating replicates using the estimate as the truth to produce clones of the estimate, with inference deriving from the clone distribution. It avoids prospective finite-dimensional model assumptions, but it makes no probabilistic claims concerning the truth; it suggests an alternative system of uncertainty quantification that is operable in nonparametric function estimation. The procedures are demonstrated using examples of smoothing spline ANOVA models in nonparametric regression. The paradigm also applies in parametric regression, where the proposed inference closely resembles traditional inference operation-wise. Conceptual discussions are scattered throughout.
Comments17 pages, 5 figures
