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.
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.
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DOI：
10.1186/1475-2875-8-216
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发表时间：
2009-09-21
期刊：
影响因子：
3
通讯作者：
Novak RJ
中科院分区：
文献类型：
--
作者：
Jacob BG;Griffith DA;Muturi EJ;Caamano EX;Githure JI;Novak RJ
阿拉伯按蚊水生生境模型的自回归回归系数通常使用全局误差技术进行评估，并以误差协方差矩阵的形式报道。然而，全球统计数据将汇总来自多个栖息地位置的误差估计。这使得很难确定哪里有An簇。阿拉伯滨水生生境的可接受预测。因此，进行某种形式的空间误差分析来检测An的簇是有用的。基于单个采样生境的不确定性残差的阿拉伯滨水生生境。本文提出了一种利用自相关指数和特征函数空间滤波器对空间模拟模型进行误差估计的方法，以区分参数不确定性对生态采样按蚊水生栖息地协变量随机模拟的影响。一种诊断检测误差残差的检测方法。基于残差自协方差矩阵参数估计的渐近分布，阿拉伯滨水生栖息地模型可以实现针对生产性栖息地集群的干预努力。本研究中考虑的模型扩展了以前在文献中考虑的正态回归分析。2006年7月至2007年12月，在肯尼亚Mwea的Karima水稻村综合设施收集了现场和远程采样数据。使用SAS 9.1.4®来探索单变量统计、相关性、分布，并从生态采样数据集生成全局自相关统计。还利用SAS/GIS®数据库中的空间协方差参数（即Moran指数）生成了本地自相关指数。Moran's统计量使用泊松模型（即负二项回归）分解为正交和不相关的合成地图模式分量。然后使用马尔可夫链蒙特卡罗（MCMC）算法使用空间构型矩阵的特征函数值来定义先验分布的期望。在WinBUGS 1.4.3®中定义了一组后验均值。在模型收敛后，使用条件分布中的样本来总结参数的后验分布。然后，利用自协方差误差矩阵，利用空间残差趋势分析来评估模型中方差不确定性的传播。通过在贝叶斯框架中指定系数估计，发现协变量分蘖数是一个显著的预测因子，与An呈正相关。Arabiensis水生栖息地。空间滤波模型约占生态样区冗余位置信息的19%。Arabiensis水生生境资料。在残差估计模型中，基于对数变换的幼虫/蛹数据和采样的栖息地协变量深度，存在显著的正自相关（即生境在地理空间中的聚类）。自相关误差协方差矩阵和空间滤波分析提供了一种计算上有吸引力和可行的方差不确定性估计描述，可以为正确识别高产An群集提供优先级蚊虫控制策略。基于幼虫/蛹产量的阿拉伯滨水生生境。
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. 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 expectations for prior distributions using a Markov chain Monte Carlo (MCMC) algorithm. A set of posterior means were defined in WinBUGS 1.4.3®. After the model had converged, samples from the conditional distributions were used to summarize the posterior distribution of the parameters. Thereafter, a spatial residual trend analyses was used to evaluate variance uncertainty propagation in the model using an autocovariance error matrix. By specifying coefficient estimates in a Bayesian framework, the covariate number of tillers was found to be a significant predictor, positively associated with An. arabiensis aquatic habitats. The spatial filter models accounted for approximately 19% redundant locational information in the ecological sampled An. arabiensis aquatic habitat data. In the residual error estimation model there was significant positive autocorrelation (i.e., clustering of habitats in geographic space) based on log-transformed larval/pupal data and the sampled covariate depth of habitat. An autocorrelation error covariance matrix and a spatial filter analyses can prioritize mosquito control strategies by providing a computationally attractive and feasible description of variance uncertainty estimates for correctly identifying clusters of prolific An. arabiensis aquatic habitats based on larval/pupal productivity.
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影响因子：
2.4
作者：
Jacob, B. G.;Griffith, D. A.;Novak, R. J.
通讯作者：
Novak, R. J.
影响因子：
5.7
作者：
Hoeting, JA;Madigan, D;Volinsky, CT
通讯作者：
Volinsky, CT
影响因子：
2.1
作者：
Dasgupta, Ranjit;Free, Heather M.;Christensen, Bruce M.
通讯作者：
Christensen, Bruce M.
DOI：
10.1111/j.2517-6161.1995.tb02015.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
DRAPER, D
通讯作者：
DRAPER, D
影响因子：
4.9
作者：
Jacob, Benjamin G.;Muturi, Ephantus J.;Novak, Robert J.
通讯作者：
Novak, Robert J.