Computational times for regression models of time-dependent exposures using nested case-control and survival analysis methodologies were compared.
We analyze this transition by non-linear regression of computational data to a hyperbolic model.
Computational methods for interpolation, regression, root finding, sorting, searching, and the solution of linear systems of equations and ordinary differential equations.
To reduce the computational complexity, a proper regression context is defined which allows fast implementation of the proposed method.
A data-driven computational framework combining Bayesian regression for imperfection-sensitive quantities of interest, uncertainty quantification and multi-objective optimization is developed for the design of complex structures.
A computational method (multiple linear regression analysis) is used to define a mathematical model that can estimate the carbonation coefficient as a function of a set of conditioning factors.
The numerical simulation results show CF-FORELM demands less computational burden than Dynamic Regression ELM (DR-ELM), and CF-FOKELM also owns higher computational efficiency than both FOKELM and online sequential ELM with kernels (OS-ELMK), and CF-FORELM is less sensitive to model parameters than CF-FOKELM.
To infer the GRN we have applied: three computational intelligence methods Least Angle Regression (LARS), Expectation Maximization (EM) with Kalman Filter (KF), and an Evolving Fuzzy Neural Network (EFuNN).
The added computational complexity of linear regression with three parameters is not justified by the improved modelling accuracy.
Fourth, case-centered logistic regression reduces computational burdens dramatically.
Furthermore, PennCNV also integrates a computational approach by fitting regression models with GC content to overcome "genomic waves".
