頁籤選單縮合
題 名 | A Generalized Convolution Model for Multivariate Nonstationary Spatial Processes |
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作 者 | Majumdar, Anandamayee; Paul, Debashis; Bautista, Dianne; | 書刊名 | Statistica Sinica |
卷 期 | 20:2 2010.04[民99.04] |
頁 次 | 頁675-695 |
分類號 | 319.5 |
關鍵詞 | Convolution; Nonstationary process; Posterior inference; Predictive distribution; Spatial statistics; Spectral density; |
語 文 | 英文(English) |
英文摘要 | Abstract: We propose a flexible class of nonstationary stochastic models for multivariate spatial data. The method is based on convolutions of spatially varying covariance kernels and produces mathematically valid covariance structures. This method generalizes the convolution approach suggested by Majumdar and Gelfand (2007) to extend multivariate spatial covariance functions to the nonstationary case. A Bayesian method for estimation of the parameters in the covariance model based on a Gibbs sampler is proposed, then applied to simulated data. Model comparison is performed with the coregionalization model of Wackernagel (2003) that uses a stationary bivariate model. Based on posterior prediction results, the performance of our model appears to be considerably better. |
本系統中英文摘要資訊取自各篇刊載內容。