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While the primary focus will be on the linear model with continuous outcomes (i.e., the classic regression framework) we will also discuss binary, categorical, and ordinal outcomes.
We consider estimation in a high-dimensional linear model with strongly correlated variables.
Optimal design and refinement of the linear model with applications to repeated measurements designs.
Combination of a linear model with a linear parameter varying model approximates the nonlinear behavior.
First, a linear model with input saturation is established from the general equation of motion.
This results into a mixed-integer non linear model with a non-convex continuous relaxation.
generalized linear model with stepwise feature selection.
(Linear Model with Skew-Normal Errors).
DRAGON achieves a linear model with respect to graph size.
Consider univariate linear model with respect to restricted parameter set: (2.10).
Calibration curve was obtained by linear model with weighted 1/x2 regression analysis.
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