标题： 平稳过程线性预测的枢轴推断 显示英文标题

标题： Pivotal inference for linear predictions in stationary processes

摘要： 在本文中，我们为平稳过程中的线性预测的最终预测误差（FPE）和相对最终预测误差（RFPE）开发了关键推断方法。我们的方法基于一种新颖的自归一化技术，避免了对经验自协方差渐近方差的估计。我们提供了（R）FPE的关键置信区间，开发了用于获得预定预测精度所需的线性预测最小阶数的估计，并还提出了针对（R）FPE超过给定阈值的假设的关键统计检验。此外，我们为偏自相关提供了新的（关键）推断工具，这些工具不需要自回归过程的假设。 显示英文摘要

摘要： In this paper we develop pivotal inference for the final (FPE) and relative final prediction error (RFPE) of linear forecasts in stationary processes. Our approach is based on a novel self-normalizing technique and avoids the estimation of the asymptotic variances of the empirical autocovariances. We provide pivotal confidence intervals for the (R)FPE, develop estimates for the minimal order of a linear prediction that is required to obtain a prespecified forecasting accuracy and also propose (pivotal) statistical tests for the hypotheses that the (R)FPE exceeds a given threshold. Additionally, we provide new (pivotal) inference tools for the partial autocorrelation, which do not require the assumption of an autoregressive process.