论文标题
通用自回旋模型的有限样本推断
Finite sample inference for generic autoregressive models
论文作者
论文摘要
自回归模型是时间序列模型，在应用和理论统计中都很重要。通常，时间序列模型的推论设备（例如置信度和假设检验）需要细微的渐近论点和构造。我们提供了一种简单的替代方案，用于使用数据拆分方法来构建有限样本有效的推论设备。我们证明了我们的构造的有效性以及相关的顺序推理工具的有效性。提出了一组仿真研究，以证明我们方法的适用性。
Autoregressive models are a class of time series models that are important in both applied and theoretical statistics. Typically, inferential devices such as confidence sets and hypothesis tests for time series models require nuanced asymptotic arguments and constructions. We present a simple alternative to such arguments that allow for the construction of finite sample valid inferential devices, using a data splitting approach. We prove the validity of our constructions, as well as the validity of related sequential inference tools. A set of simulation studies are presented to demonstrate the applicability of our methodology.
