INFILL ASYMPTOTICS FOR A STOCHASTIC PROCESS MODEL WITH MEASUREMENT ERROR
INFILL ASYMPTOTICS FOR A STOCHASTIC PROCESS MODEL WITH MEASUREMENT ERROR
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DOI：
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发表时间：
2000
影响因子：
1.4
通讯作者：
Huann-Sheng Chen;D. Simpson;Z. Ying
中科院分区：
文献类型：
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作者：
Huann-Sheng Chen;D. Simpson;Z. Ying
在空间建模中，测量误差或“块”的存在会对参数估计的样本行为产生很大的影响。本文研究了奥恩斯坦-乌伦贝克加加性白噪声的一维空间模型的最大似然估计的核块效应。在填充渐近条件下，随着样本量的增加，在越来越细的网格上采样一个紧凑的区间，得到一致性分布和渐近分布。空间填充渐近性与时间序列分析中常见的递增域渐近性有很大的不同。测量误差的一个显著影响是参数向量的Ornstein-Uhlenbeck分量的MLE只有四根n一致，而测量误差方差的MLE具有通常的根n率。
In spatial modeling the presence of measurement error, or "nugget", can have a big impact on the sample behavior of the parameter estimates. This article investigates the nugget effect on maximum likelihood estimators for a one- dimensional spatial model: Ornstein-Uhlenbeck plus additive white noise. Consis- tency and asymptotic distributions are obtained under infill asymptotics, in which a compact interval is sampled over a finer and finer mesh as the sample size increases. Spatial infill asymptotics have a very different character than the increasing domain asymptotics familiar from time series analysis. A striking effect of measurement er- ror is that MLE for the Ornstein-Uhlenbeck component of the parameter vector is only fourth-root-n consistent, whereas the MLE for the measurement error variance has the usual root-n rate.