Measurement Error in Linear Autoregressive Models
线性自回归模型中的测量误差AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要明确研究线性自回归模型及时间序列测量误差，直接对应时间序列方向。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
关注这篇论文的作者
线性自回归模型中的测量误差
- 研究问题
- 当时间序列包含加性、无相关且可能异方差的测量误差时，如何估计线性自回归参数，并评价忽略误差造成的影响？
- 方法与思路
- 作者修正 Yule–Walker 估计方程，分析朴素估计量的渐近性质，并比较利用 Kalman 滤波计算的正态伪似然等方法。
- 材料陈述的贡献
- 摘要称，修正方程估计易于计算且可适应不等测量误差方差；理论和模拟比较表明，它在所考察情形中常具有相对较高效率。
- 阅读关注点
- 核对测量误差与潜在过程的关系、方差信息需求、异方差条件，以及不同估计方法的渐近目标、效率和模拟比较设置。
- 阅读边界
- 来源为稿件版本未注明的 OpenAlex 索引摘要；方差信息的可得性和详细条件未说明，不能推广至相关测量误差或声称普遍效率优势。
原文摘要
Time series data are often subject to measurement error, usually the result of needing to estimate the variable of interest. Although it is often reasonable to assume that the measurement error is additive (i.e., the estimator is conditionally unbiased for the missing true value), the measurement error variances often vary as a result of changes in the population/process over time and/or changes in sampling effort. In this article we address estimation of the parameters in linear autoregressive models in the presence of additive and uncorrelated measurement errors, allowing heteroscedasticity in the measurement error variances. We establish the asymptotic properties of naive estimators that ignore measurement error and propose an estimator based on correcting the Yule–Walker estimating equations. We also examine a pseudo-likelihood method based on normality assumptions and computed using the Kalman filter. We review other techniques that have been proposed, including two that require no information about the measurement error variances, and compare the various estimators both theoretically and via simulations. The estimator based on corrected estimating equations is easy to obtain and readily accommodates (and is robust to) unequal measurement error variances. Asymptotic calculations and finite-sample simulations show that it is often relatively efficient.