Name
Abstract | ||
|---|---|---|
We study a class of models at the interface between statistics and numerical analysis. Specifically, we consider nonparametric regression models for the estimation of spatial fields from pointwise and noisy observations, which account for problem-specific prior information, described in terms of a partial differential equation governing the phenomenon under study. The prior information is incorporated in the model via a roughness term using a penalized regression framework. We prove the well-posedness of the estimation problem, and we resort to a mixed equal order finite element method for its discretization. Moreover, we prove the well-posedness and the optimal convergence rate of the proposed discretization method. Finally the smoothing technique is extended to the case of areal data, particularly interesting in many applications. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1137/130925426 | SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION |
Keywords | Field | DocType |
mixed finite element method,fourth order problems,nonparametric regression,smoothing | Applied mathematics,Discretization,Mathematical optimization,Polynomial regression,Nonparametric regression,Smoothing,Rate of convergence,Partial differential equation,Mathematics,Mixed finite element method,Pointwise | Journal |
Volume | Issue | ISSN |
2 | 1 | 2166-2525 |
Citations | PageRank | References |
2 | 0.67 | 2 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Laura Azzimonti | 1 | 8 | 1.63 |
| Fabio Nobile | 2 | 336 | 29.63 |
| Laura M. Sangalli | 3 | 43 | 5.37 |
| Piercesare Secchi | 4 | 70 | 11.12 |