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題 名 | Infill Asymptotics for a Stochastic Process Model with Measurement Error |
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作 者 | Chen,Huann-sheng; Simpson,Douglas G.; Ying,Zhiliang; | 書刊名 | Statistica Sinica |
卷 期 | 10:1 2000.01[民89.01] |
頁 次 | 頁141-156 |
分類號 | 319.16 |
關鍵詞 | 漸近正態性; 高斯法; 隨機過程; Asymptotic normality; Consistency; Covariance; Gaussian process; Identifiability; Maximum likelihood estimator; Measurement error; Rate of convergence; |
語 文 | 英文(English) |
英文摘要 | 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 nuggest effect on maximum likelihood estimators for a one-dimensional spatial model: Ornstein-Uhlenbeck plus additive white noise. Consistency 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 increase. Spatial infill asymptotics have a very different character than the increasing domain asymptotics familiar for time series analysis. A striking effect of measurement error is that MLE for the Ornstein-Uhlenback 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. |
本系統中英文摘要資訊取自各篇刊載內容。