GeoDa and Spatial Regression Modeling
... From an estimation point of view, the problem with an OLS model specification when spatial autocorrelation is present, is that the spatial lag term contains the dependent variables for neighboring observations, which in turn contain the spatial lag for their neighbors, and so on, leading to simultan ...
... From an estimation point of view, the problem with an OLS model specification when spatial autocorrelation is present, is that the spatial lag term contains the dependent variables for neighboring observations, which in turn contain the spatial lag for their neighbors, and so on, leading to simultan ...
Factors influencing limit values for pine needle litter decomposition
... with an extremely low decomposition rate, and as such deserves exploration in terms of the factors (abiotic and biotic) that influence it. Although limit values likely do not indicate completely undecomposable organic matter, the residual organic matter remaining once the limit value has been reache ...
... with an extremely low decomposition rate, and as such deserves exploration in terms of the factors (abiotic and biotic) that influence it. Although limit values likely do not indicate completely undecomposable organic matter, the residual organic matter remaining once the limit value has been reache ...
Classification Methods
... One can also use the percentage of data in each leaf of the tree to have an estimated probability that an observation (e.g. person) belongs to a given class. The purity of the leaf can indicate the probability an observation which “reaches that leaf” belongs to a class. In our case, the probability ...
... One can also use the percentage of data in each leaf of the tree to have an estimated probability that an observation (e.g. person) belongs to a given class. The purity of the leaf can indicate the probability an observation which “reaches that leaf” belongs to a class. In our case, the probability ...
Hydra-MIP: Automated Algorithm Configuration and Selection for
... each iteration, we add k promising configurations to the portfolio, rather than just the single best. If algorithm configuration runs were inexpensive, this modification to H YDRA would not help: additional configurations could always be found in later iterations, if they indeed complemented the por ...
... each iteration, we add k promising configurations to the portfolio, rather than just the single best. If algorithm configuration runs were inexpensive, this modification to H YDRA would not help: additional configurations could always be found in later iterations, if they indeed complemented the por ...