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In this work, we present a multi-temporal uncertainty-based method that incorporates regression models to establish the risk (probability) of land cover change (RLCC), as a function of a set of environmental and socioeconomic driving factors.
The approach we outline permits multiple DLs, incorporates regression parameters, and applies multiple imputation to account correctly for interval-measured data and to allow unbiased inference.
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These models incorporate regression coefficients that associate study characteristics (i.e., moderators) to study outcomes while allowing for unexplained variance in the model (Raudenbush [2009]).
Poisson multiple regression models, incorporating penalized regression splines to control for covariates and dealing with Poisson overdispersion, were used.
This model incorporates multivariate regression and model selection and can be fit with an expectation maximization algorithm, we call Cox-assisted clustering.
Additionally, tools that incorporate regression trees are interesting and highly accurate, providing immediately accessible and useful information in visual formats on critical machine performance for the design engineer.
Previously, the presence of regression was considered to be a risk factor for SLN metastasis; however, based on currently available data, standard clinical practice does not generally incorporate regression as a criterion for SLNB in patients with thin melanomas [ 8, 37– 37].
In order to pragmatically incorporate the regression model as a software model tool, it needs to generate effective urban simulations consistent with GIS data inputs, outputs and related functionality.
The idea of incorporating regularized regression is that majority of the top Laplacian eigenfunction entries are very close to zero and represent random noise rather than true signals of population structure differentiation.
A modified grid search algorithm, incorporating multivariable regression, was used for parameter optimisation.
Penalized splines were incorporated into regression models to assess possible nonlinearity in mortality risk.
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