Name
Abstract | ||
|---|---|---|
We use the H-matrix technology to compute the approximate square root of a covariance matrix in linear cost. This allows us to generate normal and log-normal random fields on general point sets with optimal cost. We derive rigorous error estimates which show convergence of the method. Our approach requires only mild assumptions on the covariance function and on the point set. Therefore, it might be also a nice alternative to the circulant embedding approach which applies only to regular grids and stationary covariance functions. |
Year | Venue | Field |
|---|---|---|
2018 | Numerische Mathematik | Covariance function,Mathematical optimization,Estimation of covariance matrices,Random field,Mathematical analysis,Matrix (mathematics),Rational quadratic covariance function,Circulant matrix,Covariance matrix,Mathematics,Covariance |
DocType | Volume | Issue |
Journal | 140 | 3 |
Citations | PageRank | References |
1 | 0.37 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M Feischl | 1 | 52 | 7.67 |
| Frances Y. Kuo | 2 | 479 | 45.19 |
| Ian H. Sloan | 3 | 1180 | 183.02 |