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

Abstract 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. Th...

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Published in:Malaria Journal
Main Authors: Githure John I, Caamano Erick X, Muturi Ephantus J, Griffith Daniel A, Jacob Benjamin G, Novak Robert J
Format: Article in Journal/Newspaper
Language:English
Published: BMC 2009
Subjects:
Online Access:https://doi.org/10.1186/1475-2875-8-216
https://doaj.org/article/f9d2eca6a5784f71af8d24603ca6d02c
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spelling ftdoajarticles:oai:doaj.org/article:f9d2eca6a5784f71af8d24603ca6d02c 2023-05-15T15:15:20+02:00 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 Githure John I Caamano Erick X Muturi Ephantus J Griffith Daniel A Jacob Benjamin G Novak Robert J 2009-09-01T00:00:00Z https://doi.org/10.1186/1475-2875-8-216 https://doaj.org/article/f9d2eca6a5784f71af8d24603ca6d02c EN eng BMC http://www.malariajournal.com/content/8/1/216 https://doaj.org/toc/1475-2875 doi:10.1186/1475-2875-8-216 1475-2875 https://doaj.org/article/f9d2eca6a5784f71af8d24603ca6d02c Malaria Journal, Vol 8, Iss 1, p 216 (2009) Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 article 2009 ftdoajarticles https://doi.org/10.1186/1475-2875-8-216 2022-12-31T04:55:21Z Abstract 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 it difficult to identify where there are clusters of An. arabiensis aquatic habitats of acceptable prediction. It is therefore useful to conduct some form of spatial error analysis to detect clusters of An. arabiensis aquatic habitats based on uncertainty residuals from individual sampled habitats. In this research, a method of error estimation for spatial simulation models was demonstrated using autocorrelation indices and eigenfunction spatial filters to distinguish among the effects of parameter uncertainty on a stochastic simulation of ecological sampled Anopheles aquatic habitat covariates. A test for diagnostic checking error residuals in an An. arabiensis aquatic habitat model may enable intervention efforts targeting productive habitats clusters, based on larval/pupal productivity, by using the asymptotic distribution of parameter estimates from a residual autocovariance matrix. The models considered in this research extends a normal regression analysis previously considered in the literature. Methods Field and remote-sampled data were collected during July 2006 to December 2007 in Karima rice-village complex in Mwea, Kenya. SAS 9.1.4 ® was used to explore univariate statistics, correlations, distributions, and to generate global autocorrelation statistics from the ecological sampled datasets. A local autocorrelation index was also generated using spatial covariance parameters (i.e., Moran's Indices) in a SAS/GIS ® database. The Moran's statistic was decomposed into orthogonal and uncorrelated synthetic map pattern components using a Poisson model with a gamma-distributed mean (i.e. negative binomial regression). The eigenfunction values from the spatial configuration matrices were then used to define ... Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Malaria Journal 8 1
institution Open Polar
collection Directory of Open Access Journals: DOAJ Articles
op_collection_id ftdoajarticles
language English
topic Arctic medicine. Tropical medicine
RC955-962
Infectious and parasitic diseases
RC109-216
spellingShingle Arctic medicine. Tropical medicine
RC955-962
Infectious and parasitic diseases
RC109-216
Githure John I
Caamano Erick X
Muturi Ephantus J
Griffith Daniel A
Jacob Benjamin G
Novak Robert J
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
topic_facet Arctic medicine. Tropical medicine
RC955-962
Infectious and parasitic diseases
RC109-216
description Abstract 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 it difficult to identify where there are clusters of An. arabiensis aquatic habitats of acceptable prediction. It is therefore useful to conduct some form of spatial error analysis to detect clusters of An. arabiensis aquatic habitats based on uncertainty residuals from individual sampled habitats. In this research, a method of error estimation for spatial simulation models was demonstrated using autocorrelation indices and eigenfunction spatial filters to distinguish among the effects of parameter uncertainty on a stochastic simulation of ecological sampled Anopheles aquatic habitat covariates. A test for diagnostic checking error residuals in an An. arabiensis aquatic habitat model may enable intervention efforts targeting productive habitats clusters, based on larval/pupal productivity, by using the asymptotic distribution of parameter estimates from a residual autocovariance matrix. The models considered in this research extends a normal regression analysis previously considered in the literature. Methods Field and remote-sampled data were collected during July 2006 to December 2007 in Karima rice-village complex in Mwea, Kenya. SAS 9.1.4 ® was used to explore univariate statistics, correlations, distributions, and to generate global autocorrelation statistics from the ecological sampled datasets. A local autocorrelation index was also generated using spatial covariance parameters (i.e., Moran's Indices) in a SAS/GIS ® database. The Moran's statistic was decomposed into orthogonal and uncorrelated synthetic map pattern components using a Poisson model with a gamma-distributed mean (i.e. negative binomial regression). The eigenfunction values from the spatial configuration matrices were then used to define ...
format Article in Journal/Newspaper
author Githure John I
Caamano Erick X
Muturi Ephantus J
Griffith Daniel A
Jacob Benjamin G
Novak Robert J
author_facet Githure John I
Caamano Erick X
Muturi Ephantus J
Griffith Daniel A
Jacob Benjamin G
Novak Robert J
author_sort Githure John I
title 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
title_short 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_sort 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
publisher BMC
publishDate 2009
url https://doi.org/10.1186/1475-2875-8-216
https://doaj.org/article/f9d2eca6a5784f71af8d24603ca6d02c
geographic Arctic
geographic_facet Arctic
genre Arctic
genre_facet Arctic
op_source Malaria Journal, Vol 8, Iss 1, p 216 (2009)
op_relation http://www.malariajournal.com/content/8/1/216
https://doaj.org/toc/1475-2875
doi:10.1186/1475-2875-8-216
1475-2875
https://doaj.org/article/f9d2eca6a5784f71af8d24603ca6d02c
op_doi https://doi.org/10.1186/1475-2875-8-216
container_title Malaria Journal
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