Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Carlos E. Melo"'
Publikováno v:
Revista Chilena de Anestesia, Vol 50, Iss 3, Pp 520-525 (2021)
Externí odkaz:
https://doaj.org/article/f1ba0d1cc6dc481a82ed9b2ab977f886
Publikováno v:
Journal of Geophysics and Engineering. 20:523-537
Traditional and modern spatial prediction techniques are applied in the analysis of water quality, evidencing a new approach that allows the modeling of a hydrogeological system in the central area of Boyacá, Colombia. The objective is to determine
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::14770e95f71ac2bd61ae22ed12ff7804
https://doi.org/10.1093/jge/gxad023
https://doi.org/10.1093/jge/gxad023
Publikováno v:
Revista Chilena de Anestesia, Vol 50, Iss 3, Pp 520-525 (2021)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3792fa55559f288f248bd09cebd5ab0f
https://revistachilenadeanestesia.cl/PII/revchilanestv50n03-15.pdf
https://revistachilenadeanestesia.cl/PII/revchilanestv50n03-15.pdf
Publikováno v:
BASE-Bielefeld Academic Search Engine
A new method based on distances for modeling continuous random data in Gaussian random fields is presented. In non-stationary cases in which a trend or drift is present, dealing with information in regionalized mixed variables (including categorical,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fe5471645e3c872026c931b1862023f8
Publikováno v:
Spatial Statistics. 19:1-20
According to Cressie (1991), the median polish kriging (MPK) is a hybrid method for predicting of spatial data in a two dimensional surface. It combines a kriging interpolator and an analysis of tables. As a result of the effect of a directional tren
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c408b7c8eb8beeee41d358a22d2e892e
https://doi.org/10.1016/j.spasta.2016.10.003
https://doi.org/10.1016/j.spasta.2016.10.003
Publikováno v:
Statistica Neerlandica. 70:47-76
We propose a beta spatial linear mixed model with variable dispersion using Monte Carlo maximum likelihood. The proposed method is useful for those situations where the response variable is a rate or a proportion. An approach to the spatial generaliz
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a1539d6bed588c51d5225915c4c65d3a
https://doi.org/10.1111/stan.12078
https://doi.org/10.1111/stan.12078
Publikováno v:
Journal of Applied Statistics. 43:1198-1225
We present a solution to problems where the response variable is a count, a rate or binary using a generalised linear space–time autoregressive model with space–time autoregressive disturbances (GLSTARAR). The possibility to test the fixed effect
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7af129fdb7178d4c907456811fb41891
https://doi.org/10.1080/02664763.2015.1092506
https://doi.org/10.1080/02664763.2015.1092506
Publikováno v:
BASE-Bielefeld Academic Search Engine
In the context of regression with a beta-type response variable, we propose a new method that links two methodologies: a distance-based model, and a beta regression with variable dispersion. The proposed model is useful for those situations where the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::abe79014d07e6f321d3a0ef0c8efcb36
https://doi.org/10.1007/s10182-014-0232-6
https://doi.org/10.1007/s10182-014-0232-6
Publikováno v:
European Journal of Public Health. 25
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dbd47d9178e348683ed6ff8dadafeb84
https://doi.org/10.1093/eurpub/ckv175.229
https://doi.org/10.1093/eurpub/ckv175.229
Publikováno v:
BASE-Bielefeld Academic Search Engine
In the context of local interpolators, radial basis functions (RBFs) are known to reduce the computational time by using a subset of the data for prediction purposes. In this paper, we propose a new distance-based spatial RBFs method which allows mod
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::14536d1757a32f1d4647ee3b9155d224
http://link.springer.com/10.1007/s10182-017-0305-4
http://link.springer.com/10.1007/s10182-017-0305-4