spatial regression
Estimation in semiparametric spatial regression
... and regression, but no such development has taken place for spatial ...For spatial data on a grid evaluat- ing the conditional mean given its closest neighbours requires a four-dimensional ...
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Paddy Availability Modeling in Indonesia Using Spatial Regression
... in regression analysis will not be accurate in sol- ving problems which contain outliers or extreme obser- ...the regression parameters is considered inappropriate (Su- santi and Pratiwi [5], Yohai ...
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Birnbaum-Saunders spatial regression models: Diagnostics and application to chemical data
... The main objective of this article is to derive diagnostic methods for BS spatial regression models. On the one hand, assessment of global influence uses the single case-deletion method proposed by Pan et ...
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Foreign direct investment in provinces: A spatial regression approach to FDI in Vietnam
... for spatial regression models not only because of data availability at the provincial level, but also because, during the period under investigation (2006-2009), the FDI/GDP ratio for Vietnam provinces has ...
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Spatial variability of excess mortality during prolonged dust events in a high-density city: a time-stratified spatial regression approach
... applied spatial delineation techniques to stratify socioeconomic or environmental data by groups [27, 48, ...capture spatial differences between groups, but is still insufficient to estimate the individual ...
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Estimation in semiparametric spatial regression
... Estimation in semiparametric spatial regression Gao, Jiti and Lu, Zudi and Tjostheim, Dag The University of Adelaide, London School of Economics, The University of Bergen... marginal add[r] ...
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Note on nonparametric $M$-estimation for spatial regression
... years spatial statistics has been widely applied in diverse areas such as climatology, epidemiol- ogy, agronomy, meteorology, econometrics, image processing, ...on spatial models, see, for example, the ...
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Probabilistic Spatial Regression using a Deep Fully Convolutional Neural Network
... However, regression using CNNs is not usually probabilistic [8, ...probabilistic regression method was proposed in [15] to address this ...Euclidean regression loss function to enforce a set of known ...
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A poisson regression approach for modelling spatial autocorrelation between geographically referenced observations
... in spatial regression requires acknowl- edgment of residual spatial dependence, since regression coefficients of interest will often be sensitive to the form of the dependence assumed, as was ...
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Ten Spatial Problems with myGeoffice© for Teaching Purposes
... Weighted Regression (GWR) is one of several spatial regression techniques that provides a local model of the variable the researcher are trying to understand/predict (dependent variable) by fitting a ...
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Model uncertainty in matrix exponential spatial growth regression models
... for spatial regression models, namely the appropriate spatial weight matrix to be employed and the appropriate explanatory vari- ...the spatial Durbin model (SDM) specification in this study ...
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Incorporating spatial variation in housing attribute prices: A comparison of geographically weighted regression and the spatial expansion method
... Geographically Weighted Regression (GWR) is a local modeling approach that explicitly allows parameter estimates to vary over space (Brunsdon et al. 1996). Rather than specifying a single model to characterize the ...
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Logistic regression of southern pine beetle outbreaks with spatial and temporal autocorrelation
... In the proposed method the proportion of years in outbreak is predicted using a marginal logistic regression model with spatial autocorrelation among counties, with adjustment of varianc[r] ...
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Geospatial examination of lithium in drinking water and suicide mortality
... Global regression models assume a homogeneous be- havior of the estimated parameters across space, which has often proven to be ...Weighted Regression (GWR), a local regression model that explores ...
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Integration of precision farming data and spatial statistical modelling to interpret field scale maize grain yield variability in New Zealand
... captured spatial data and publicly-available satellite imagery and climatic records, there is potential to use this information to inform farm management ...appropriate spatial analysis techniques are still ...
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Local attribute-similarity weighting regression algorithm for interpolating soil property valuesLocal attribute-similarity weighting regression algorithm for interpolating soil property values
... understand spatial relationships between environmental variables and the response ...the spatial points whose environmental attributes were very similar to those of the point to be estimated, and who might ...
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A wavelet-based method to remove spatial autocorrelation in the analysis of species distributional data
... Our WRM model and the non-spatial model GLM differ obviously in the spatial signature of their residuals. Figure 2 shows that residual autocorrelation measured by Moran’s I is considerably reduced compared ...
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Using geographically weighted regression to explore spatial variation in survey data
... This paper aims to move away from a “one size fits all” approach to modelling survey nonresponse across the UK and explores possible spatial variation in responding behaviour. Using data from the UK wave of the ...
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The Relationship between R&D Investment and Dividend Payment Tax Incentives and Their Role in the Dividend Tax Puzzle
... Waber and colleagues (1994) used the ROCF to explore executive function deficits in a group of long-term survivors of childhood acute lymphoblastic leukemia (ALL). Survivors have been shown to exhibit increased ...
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Enhanced spatial models for predicting the geographic distributions of tick-borne pathogens
... the spatial autoregressive model had a higher AUC than the global environmental ...or spatial processes such as dispersal that can create patterns that are unrelated to the ...that spatial ...
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