Search Results - subject:"variogram"

Summary characteristics for multivariate function‐valued spatial point process attributes

Eckardt, Matthias; Comas, Carles; Mateu, Jorge - In: International Statistical Review 93 (2024) 1, pp. 150-178

Prompted by modern technologies in data acquisition, the statistical analysis of spatially distributed function‐valued quantities has attracted a lot of attention in recent years. In particular, combinations of functional variables and spatial point processes yield a highly challenging...

Persistent link: https://www.econbiz.de/10015411040

Static and dynamic models for multivariate distribution forecasts : proper scoring rule tests of factor-quantile versus multivariate GARCH models

Alexander, Carol; Han, Yang; Meng, Xiaochun - In: International journal of forecasting 39 (2023) 3, pp. 1078-1096

Persistent link: https://www.econbiz.de/10014465245

Land price polarization and dispersion in Tokyo : a spatial model approach

Kanno, Yuta; Shiohama, Takayuki - In: Asia-Pacific journal of regional science 6 (2022) 2, pp. 807-835

Persistent link: https://www.econbiz.de/10013279466

Incorporating neighborhood scale effects into land loss modeling using semivariograms

Cai, Heng; Lam, Nina S. N.; Zou, Lei - In: Journal of geographical systems : geographical … 24 (2022) 3, pp. 419-439

Persistent link: https://www.econbiz.de/10013462070

So close yet so unequal: Reconsidering spatial inequality in U.S. cities

Andreoli, Francesco; Peluso, Eugenio - 2017

Spatial income inequality in cities is assessed by looking at the distribution of income across individuals and their neighbors. Two new Gini-type spatial inequality indices are introduced: the first index measures the average degree of income inequality within individual neighborhoods; the...

Persistent link: https://www.econbiz.de/10011739629

So close yet so unequal : reconsidering spatial inequality in U.S. cities

Andreoli, Francesco; Peluso, Eugenio - 2017 - Preliminary version: February 2, 2017

Persistent link: https://www.econbiz.de/10011776460

Forecasting interest rates using geostatistical techniques

Arbia, Giuseppe; Di Marcantonio, Michele - In: Econometrics 3 (2015) 4, pp. 733-760

Kriging method based on the anisotropic variogram. Furthermore, a comparison with other recent methods for forecasting yield …

Persistent link: https://www.econbiz.de/10011755309

Forecasting interest rates using geostatistical techniques

Arbia, Giuseppe; Di Marcantonio, Michele - In: Econometrics : open access journal 3 (2015) 4, pp. 733-760

Kriging method based on the anisotropic variogram. Furthermore, a comparison with other recent methods for forecasting yield …

Persistent link: https://www.econbiz.de/10011411696

Spatial Variability Analysis of Cu Content: A Case Study in Jiurui Copper Mining Area

Hoang, Huy A.; Vu, Tuyen D.; Nguyen, Thanh T. - In: International Journal of Applied Geospatial Research (IJAGR) 8 (2017) 1, pp. 81-93

Conventional variogram has been widely applied to study spatial variability of geochemical data. In case of data is not … normally distributed, the conventional estimator is biased. In this study, Cressie variogram and Moran correlogram were used to …. Cressie variogram was applied to reduce the influences of high values in identifying spatial variability in different …

Persistent link: https://www.econbiz.de/10012042824

Spatial circular matrices, with applications

Hillier, Grant; Martellosio, Federico - 2010

The cumulants of the quadratic forms associated to the so-called spatial design matrices are often needed for inference in the context of isotropic processes on uniform grids. Unfortunately, because the eigenvalues of the matrices involved are generally unknown, the computation of the cumulants...

Persistent link: https://www.econbiz.de/10010288389