Estimadores de Semivariância: Uma Revisão
DOI:
https://doi.org/10.5902/2179460X21326Keywords:
Geoestatística, Estimador, Semivariância, Robustez, Revisão.Abstract
A Geoestatística é um ramo da estatística responsável pela incorporação e entendimento das dependências espaciais na modelagemde variáveis georreferenciadas. Na busca pelo melhor modelo ajustado, tem-se o desafio de desenvolver e dominar um ferramental
que permita a análise, e quantificação, da variabilidade espacial do fenômeno em estudo por meio de modelagens específicas, para
isso, é comum fazer uso de medidas de correlação espacial como covariância, correlação e, especialmente, semivariância, uma
medida resumo da variabilidade e dependência espacial. É então essencial um bom ajuste do semivariograma, e um estimador
adequado para a semivariância é necessário para este ajuste. Uma vez que a maioria dos métodos de estimação em Geoestatística e
algoritmos de simulação requerem um modelo teórico ajustado a uma semivariância empírica, objetivou-se expor as construções,
deduções e a ideia geral que determina a adequabilidade dos principais estimadores de semivariância a fim de prover a melhor
decisão a ser tomada. Portanto, este texto apresenta uma revisão de oito estimadores de semivariância: Clássico de Matheron,
Robusto de Cressie e Hawkins, das Medianas de Cressie, das Diferenças de Haslett, Altamente Robusto de Genton, Pairwise,
New-1 (MW1) e New-2 (MW2).
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