| Spatial regression methods capture spatial dependency in regression analysis, avoiding statistical problems such as unstable parameters and unreliable significance tests, as well as providing information on spatial relationships among the variables involved. Depending on the specific technique, spatial dependency can enter the regression model as relationships between the independent variables and the dependent, between the dependent variables and a spatial lag of itself, or in the error terms. Geographically weighted regression (GWR) is a local version of spatial regression that generates parameters disaggregated by the spatial units of analysis.[16] This allows assessment of the spatial heterogeneity in the estimated relationships between the independent and dependent variables. The use of Markov Chain Monte Carlo (MCMC) methods can allow the estimation of complex functions, such as Poisson-Gamma-CAR, Poisson-lognormal-SAR, or Overdispersed logit models. |
About us|Jobs|Help|Disclaimer|Advertising services|Contact us|Sign in|Website map|Search|
GMT+8, 2015-9-11 22:16 , Processed in 0.153797 second(s), 16 queries .
