[PDF] Top 20 An autoregressive point source model for spatial process
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An autoregressive point source model for spatial process
... ARPS process, where we observe a spatio- temporal process that can possibly alter the effect of a source over ...of source apportionment (see, for example, Henry 1997; Henry, Spiegelman, ... See full document
32
Point process models for spatio temporal distance sampling data from a large scale survey of blue whales
... continuous spatial model has the potential to borrow strength from data outside the lightly-sampled strata to improve overall ...density model by using a spatial model of density in the ... See full document
28
Change Point Estimation of Location Parameter in Multistage Processes
... order autoregressive model (AR(1)) is used to model a multistage process observations, where a X -chart is established for monitoring its ... See full document
5
A Bayesian approach to fitting Gibbs processes with temporal random effects
... consider spatial point pattern data that have been observed re- peatedly over a period of time in an inhomogeneous ...Each spatial point pattern can be regarded as a “snapshot” of the ... See full document
25
Semiparametric Estimation and Testing of Smooth Coefficient Spatial Autoregressive Models
... coefficient” spatial autoregressive model closely relates to the family of partially linear semiparametric spatial models recently proposed in the literature (Su & Jin, 2010; Su, 2012; ... See full document
46
Careful prior specification avoids incautious inference for log Gaussian Cox point processes
... the spatial point patterns formed by the two species are analysed to establish which covariates potentially influence the spatial distribution of the observed ...to model misspecification ... See full document
25
Temporal-spatial distribution of non-point source pollution in a drinking water source reservoir watershed based on SWAT
... into point source pollution and non-point source pollution (Cao et ...2003). Point source pollution is a concentration of various wastewater ...non-point source ... See full document
6
Spatial Modelling of Some Conditional Autoregressive Priors in A Disease Mapping Model the Bayesian Approach
... basic model usually used in disease mapping is the Besag, York and Mollie (BYM) model, which combines two random effects, a spatially structured and a spatially unstructured random ...Conditional ... See full document
7
A Remark on the Asymptotic Distribution of the OLS Estimator for a Purely Autoregressive Spatial Model
... The problems we have just described force us to analyse the OLS estimator more closely. The solution we have found has its limitations and advantages. Firstly, we have not been able to prove that the procedure described ... See full document
28
Spatial Distribution of Non-point Source Pollution in Vembanad Lake
... The Vembanad Lake has been subjected to various environmental studies by different agencies. The water quality study conducted by Binu & Harikumar 1 made an assessment on Eutrophication of this lake using Dynamic ... See full document
6
Spatial externalities and growth in a Mankiw Romer Weil world: Theory and evidence
... growth model that accounts for technological interdependence among regions in a Mankiw-Romer-Weil ...to spatial proximity and allow for ideas to flow to nearby regional ...theoretical model and the ... See full document
24
Comparison of the Sampling Efficiency in Spatial Autoregressive Model
... A random walk Metropolis-Hastings algorithm has been widely used in sampling the parameter of spatial interaction in spatial autoregressive model from a Bayesian point of view. In ... See full document
12
A spatial autoregressive Poisson gravity model
... additive approach assumes a lognormal distribution of the error term. If this assumption holds, then the resulting ordinary least squares (OLS) estimator is the best linear unbiased estimator (BLUE). If the log of the ... See full document
29
Simulation and application of spatial autoregressive geographically weighted regression model (SAR GWR)
... Several spatial models have been developed to accommodate the characteristics of the spatial ...the model generally developed to solve only one characteristic of the spatial ...of ... See full document
9
Spatial point process models for MRI lesion data in multiple sclerosis
... imHPGRF model struggled to predict the true mark parameter ...mark process falls short when trying to model individual mark values that are far from the ...the model as it tries to estimate a ... See full document
183
Spatial Modeling for Capturing the Effects of Point Sources
... toregressive (CAR) approach for several reasons. First, deterministic ADMs imply a natural neighborhood structure for modeling the effect of point sources; this default structure can be incorporated into a CAR ... See full document
179
Bayesian Inference in Spatial Sample Selection Models
... selection model that has a first order spatial autoregressive process in the disturbance ...selection model that has a first order spatial autoregressive process in ... See full document
33
Simulation on the Time Progress of the Non Point Source Pollution Load in Initial Stage Runoff for Small Watershed
... non-point source pollution load in single rainfall, there is few study on the process of pollution load changing with time of ...pollutant source and cumulative runoff by the monitoring value ... See full document
10
GMM estimation of Spatial Panels with Fixed Effects
... regression model with …xed e¤ects, unknown heteroskedasticity, and spatial autoregressive (SAR) ...and spatial lags both in the dependent variable and in the disturbances, under homoskedastic ... See full document
23
A stochastic marked point process model for earthquakes
... The model is based upon a Bayesian approach with user specified prior distributions for all parameters, while empir- ical data are used for deriving posterior ...the model, however, and in this paper we ... See full document
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