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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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 |
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Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
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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 |
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Arctic |
genre_facet |
Arctic |
op_source |
Malaria Journal, Vol 8, Iss 1, p 216 (2009) |
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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 |
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Malaria Journal |
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8 |
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1 |
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1766345696949043200 |