ON THE CONSISTENT SEPARATION OF SCALE AND VARIANCE FOR GAUSSIAN RANDOM FIELDS

• 成果类型: Article
• 署名作者: Anderes, Ethan
• 署名单位: University of California System; University of California Davis
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/09-AOS725
• 发表日期: 2010
• 页码: 870-893
• 关键词: Asymptotic Optimality; QUADRATIC VARIATION; linear predictions; identification; version

摘要

We present fixed domain asymptotic results that establish consistent estimates of the variance and scale parameters for a Gaussian random field with a geometric anisotropic Matern autocovariance in dimension d > 4. When d < 4 this is impossible due to the mutual absolute continuity of Matern Gaussian random fields with different scale and variance (see Zhang [J. Amer. Statist. Assoc. 99 (2004) 250-261]). Informally, when d > 4, we show that one can estimate the coefficient on the principle irregular term accurately enough to get a consistent estimate of the coefficient on the second irregular term. These two coefficients can then be used to separate the scale and variance. We extend our results to the general problem of estimating a variance and geometric anisotropy for more general autocovariance functions. Our results illustrate the interaction between the accuracy of estimation, the smoothness of the random field, the dimension of the observation space and the number of increments used for estimation. As a corollary, our results establish the orthogonality of Matern Gaussian random fields with different parameters when d > 4. The case d = 4 is still open.