A heteroskedastic error covariance matrix estimator using a first-order conditional autoregressive Markov simulation for deriving asympotical efficient estimates from ecological sampled Anopheles arabiensis aquatic habitat covariates

BACKGROUND: Autoregressive regression coefficients for Anopheles arabiensis aquatic habitat models are usually assessed using global error techniques and are reported as error covariance matrices. A global statistic, however, will summarize error estimates from multiple habitat locations. This makes...

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Detalles Bibliográficos
Autores principales: Jacob, Benjamin G, Griffith, Daniel A, Muturi, Ephantus J, Caamano, Erick X, Githure, John I, Novak, Robert J
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2009
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2760564/
https://www.ncbi.nlm.nih.gov/pubmed/19772590
http://dx.doi.org/10.1186/1475-2875-8-216

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2760564/
https://www.ncbi.nlm.nih.gov/pubmed/19772590
http://dx.doi.org/10.1186/1475-2875-8-216

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