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您的检索式：作者名="Benjamin G.JACOB"
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| 1 | Quasi-likelihood techniques in a logistic regression equation for identifying Simulium damnosum s.l.larval habitats intra-cluster covariates in Togo显示文摘The standard methods for regression analyses of clustered riverine larval habitat data of Simulium damnosum s.l.a major black-fly vector of onchoceriasis,postulate models relating observational ecological-sampled parameter estimators to prolific habitats without accounting for residual intra-cluster error correlation effects.Generally,this correlation comes from two sources:(1)the design of the random effects and their assumed covariance from the multiple levels within the regression model and(2)the correlation structure of the residuals.Unfortunately,inconspicuous errors in residual intracluster correlation estimates can overstate precision in forecasted S.damnosum s.l.riverine larval habitat explanatory attributes regardless how they are treated(e.g.independent,autoregressive,Toeplitz,etc.).In this research,the geographical locations for multiple riverine-based S.damnosum s.l.larval ecosystem habitats sampled from two preestablished epidemiological sites in Togo were identified and recorded from July 2009 to June 2010.Initially,the data were aggregated into PROC GENMOD.An agglomerative hierarchical residual cluster-based analysis was then performed.The sampled clustered study site data was then analyzed for statistical correlations using monthly biting rates(MBR).Euclidean distance measurements and terrain-related geomorphological statistics were then generated in ArcGIS.A digital overlay was then performed also in ArcGIS using the georeferenced ground coordinates of high and low density clusters stratified by annual biting rates(ABR).The data was overlain onto multitemporal sub-meter pixel resolution satellite data(i.e.QuickBird 0.61m wavbands).Orthogonal spatial filter eigenvectors were then generated in SAS/Geographic Information Systems(GIS).Univariate and nonlinear regression-based models(i.e.logistic,Poisson,and negative binomial)were also employed to determine probability distributions and to identify statistically significant parameter estimators from the sampled data.Thereafter,Durbin–Watson statistics were used to test the null hypothesis that the regression residuals were not autocorrelated against the alternative that the residuals followed an autoregressive process in AUTOREG.Bayesian uncertainty matrices were also constructed employing normal priors for each of the sampled estimators in PROC MCMC.The residuals revealed both spatially structured and unstructured error effects in the high and low ABR-stratified clusters.The analyses also revealed that the estimators,levels of turbidity,and presence of rocks were statistically significant for the high-ABR-stratified clusters,while the estimators distance between habitats and floating vegetation were important for the low-ABR-stratified cluster.Varying and constant coefficient regression models,ABRstratified GIS-generated clusters,sub-meter resolution satellite imagery,a robust residual intra-cluster diagnostic test,MBR-based histograms,eigendecomposition spatial filter algorithms,and Bayesian matrices can enable accurate autoregressive estimation of latent uncertainity affects and other residual error probabilities(i.e.heteroskedasticity)for testing correlations between georeferenced S.damnosum s.l.riverine larval habitat estimators.The asymptotic distribution of the resulting residual adjusted intra-cluster predictor error autocovariate coefficients can thereafter be established while estimates of the asymptotic variance can lead to the construction of approximate confidence intervals for accurately targeting productive S.damnosum s.l.habitats based on spatiotemporal field-sampled count data. | Benjamin G.JACOB Robert J.NOVAK Laurent TOE Moussa S.SANFO Abena N.AFRIYIE Mohammed A.IBRAHIM Daniel A.GRIFFITH Thomas R.UNNASCH | 2012 | Geo-Spatial Information Science2012，15，2： | 1 |
| 2 | 水稻种植区疟疾媒介环境生产力与地面植被覆盖的关系(英文)显示文摘本文选择了肯尼亚Mwea水稻种植区中Kangichiri、Kiuria和Rurumi3个村庄-农田交错地区为观察区,分析并比较了2种卫星数据对水稻种植区疟疾媒介分布的指示情况。首先运用2005年7月获取的Quickbird(分辨率0.6m)和Ikonos(分辨率4m)卫星数据在ErdasImagineV8.7中生成观察区的地面植被覆盖图;并于2005年的7月至2006的7月观察相应地区地面蚊虫消长情况。通过对观察区的卫星数据的最大似然法监测分类,并于分类后对每一田块与灌溉渠道都用Arc Info9.1进行栅格矢量化处理(每一栅格设置唯一的标识)。所有调查的蚊虫滋生点,依照水稻的生育期的不同分为6层进行分析。然后将经差分GPS定位的每一处水稻田及按蚊产卵点都叠加到该地区的卫星底层数据上,并对不同的水体、水稻生育期、调查地点的蚊虫滋生情况进行了方差分析。结果显示,由于Ikonos只有可见光和近红外光谱分辨能力,单一的Ikonos卫星数据难以区分不同样点和分层的生境,而QuickBird具有全光谱分辨能力,可区分所有的稻田生境。因此,可根据QuickBird0.6m卫星数据的土地利用和覆盖指数和阿拉伯按蚊滋生点幼虫的增殖特性,在当地建立和应用媒介综合防治系统(IVM-Integrated Vector Management)。 | Benjamin G.JACOB Ephantus J.MUTURI Jose E.FUNES Joseph I.SHILILU John I.GITHURE Robert J.NOVAK | 2007 | 寄生虫与医学昆虫学报2007，14，2： | 0 |
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