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Covariance Tapering for Prediction of Large Spatial Data Sets in Transformed Random Fields
http://hdl.handle.net/2261/50186
http://hdl.handle.net/2261/5018694ccd2ec-0886-47c7-b0fe-28f5ae408f22
| Item type | テクニカルレポート / Technical Report(1) | |||||
|---|---|---|---|---|---|---|
| 公開日 | 2017-01-17 | |||||
| タイトル | ||||||
| タイトル | Covariance Tapering for Prediction of Large Spatial Data Sets in Transformed Random Fields | |||||
| 言語 | ||||||
| 言語 | eng | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Covariance tapering | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Hermite polynomials | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Kriging | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Spatial statistics | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Spectral density | |||||
| キーワード | ||||||
| 主題Scheme | Other | |||||
| 主題 | Transformed random field | |||||
| 資源タイプ | ||||||
| 資源 | http://purl.org/coar/resource_type/c_18gh | |||||
| タイプ | technical report | |||||
| アクセス権 | ||||||
| アクセス権 | metadata only access | |||||
| アクセス権URI | http://purl.org/coar/access_right/c_14cb | |||||
| 著者 |
Hirano, Toshihiro
× Hirano, Toshihiro× Yajima, Yoshihiro |
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| 著者所属 | ||||||
| 著者所属 | Graduate School of Economics, University of Tokyo | |||||
| 抄録 | ||||||
| 内容記述タイプ | Abstract | |||||
| 内容記述 | The best linear unbiased predictor (BLUP) is called a kriging predictor and has been widely used to interpolate a spatially correlated random process in scientific areas such as geostatistics. In many cases, data sets in spatial problems are often so large that a kriging predictor is impractically time-consuming. To reduce the computational complexity, covariance tapering has been developed by Furrer et al. (2006) for large spatial data sets. The BLUP is identical with the conditional expectation if an underlying random field is Gaussian and consequently is the optimal predictor in the mean squared error (MSE) sense whereas if an original data takes a nonnegative value or has a skewed distribution, we frequently apply a nonlinear transformation to it to get a data which is nearer Gaussian. Then the optimality of the BLUP for the original data is unclear because it is not Gaussian. In this paper we consider covariance tapering in a class of transformed Gaussian models for random fields and show that the BLUP, the BLUP using covariance tapering and the optimal predictor are asymptotically equivalent in the MSE sense if the covariance function of the underlying Gaussian random field is Mat´ern type. This is an extension of Furrer et al. (2006). Monte Carlo simulations support theoretical results. | |||||
| 内容記述 | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | Revised in July 2012, December 2012 and January 2013; forthcoming in Annals of Institute of Statistical Mathematics. | |||||
| 内容記述 | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | 本文フィルはリンク先を参照のこと | |||||
| 書誌情報 |
Discussion paper series. CIRJE-F 巻 CIRJE-F-823, 発行日 2011-10 |
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| 書誌レコードID | ||||||
| 収録物識別子タイプ | NCID | |||||
| 収録物識別子 | AA11450569 | |||||
| フォーマット | ||||||
| 内容記述タイプ | Other | |||||
| 内容記述 | application/pdf | |||||
| 日本十進分類法 | ||||||
| 主題Scheme | NDC | |||||
| 主題 | 335 | |||||
| 出版者 | ||||||
| 出版者 | 日本経済国際共同センター | |||||
| 出版者別名 | ||||||
| Center for International Research on the Japanese Economy | ||||||
| 関係URI | ||||||
| 識別子タイプ | URI | |||||
| 関連識別子 | http://www.cirje.e.u-tokyo.ac.jp/research/dp/2011/2011cf823ab.html | |||||
