Chiara Ghiringhelli
Biographic Data
| ID | 9502017 |
|---|---|
| NAME | Chiara Ghiringhelli |
| GIVEN NAMES | Chiara |
| FAMILY NAME | Ghiringhelli |
| SIGNATURE | GHIRINGHELLI C |
| AFFILIATIONS | Department of Statistical Science Università Cattolica del Sacro Cuore Roma Italy |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 0 |
Effects of Confidentiality‐Preserving Geo‐Masking on the Estimation of Semivariogram and of the Kriging Variance
Geostatistical methods, such as semivariograms and kriging are well‐known spatial tools commonly employed in many disciplines such as health, mining, forestry, meteorology to name only few. They are based essentially on point‐referenced data on a continuous space and on the calculation of distances between them. In many practical instances, however, the exact point location, even if exactly known, is geo‐masked to preserve confidentiality. This t…
Recursive Estimation of the Spatial Error Model
In this paper, we propose a recursive approach to estimate the spatial error model. We compare the suggested methodology with standard estimation procedures and we report a set of Monte Carlo experiments which show that the recursive approach substantially reduces the computational effort affecting the precision of the estimators within reasonable limits. The proposed technique can prove helpful when applied to real‐time streams of geographical d…
No prominent works on this page.
Effects of Confidentiality‐Preserving Geo‐Masking on the Estimation of Semivariogram and of the Kriging Variance
Geostatistical methods, such as semivariograms and kriging are well‐known spatial tools commonly employed in many disciplines such as health, mining, forestry, meteorology to name only few. They are based essentially on point‐referenced data on a continuous space and on the calculation of distances between them. In many practical instances, however, the exact point location, even if exactly known, is geo‐masked to preserve confidentiality. This t…
Recursive Estimation of the Spatial Error Model
In this paper, we propose a recursive approach to estimate the spatial error model. We compare the suggested methodology with standard estimation procedures and we report a set of Monte Carlo experiments which show that the recursive approach substantially reduces the computational effort affecting the precision of the estimators within reasonable limits. The proposed technique can prove helpful when applied to real‐time streams of geographical d…
Computer Science (2 works) · Data mining (2 works) · Estimation (2 works) · Mathematics (2 works) · Soil Geostatistics and Mapping (2 works) · Spatial and Panel Data Analysis (2 works) · Statistics (2 works) · Algorithm (1 work) · Artificial Intelligence (1 work) · Artificial Intelligence (1 work)