From the parametric view, SLRT can be employed as a recursive change point detect procedure for segmented linear regression (SLR) models, which is much more efficient and flexible than the traditional grid search method. Along this way, we propose to use the conditional Kendall's 蟿 correlation...
a segmented linear-regression model is built based on the periodical variation features which help to obtain regression curves that precisely reflect the change of stockline.Finally,we take regression statistical indexes as coefficients of the regulation weights,and construct a dynamic weighting radial ...
F. Dahm (1985), Estimation of the linear-linear segmented regression model in the presence of measurement error, Commun. Stat. Theor. Meth., 14(4), 809-826.Estimation of the linear-linear segmented regression model in the presence of measurement error. Gbur, E. E,Dahm, P. F. Commun....
A two-dimensional segmented regression model was developed to describe the plant's tolerance, linear response, and saturation effect of Al and Mn individually as a plateau-linear-plateau trend. The model also included the interaction between Al and Mn defined as the multiplication of their linear ...
segmented-packageSegmentedrelationshipsinregressionmodels Description EstimationofGeneralizedLinearModelswithpiecewiselinearrelationshipshavingafixednumber ofbreak-points. Details Package:segmented Type:Package Version:0.2-8.2 Date:2011-07-05 License:GPL Packagesegmentedisaimedtoestimatelinearandgeneralizedlinearmodelshavi...
Representation of the log(10) transformed data is significantly improved by the use of a segmented regression instead of a single linear regression. There is a significant reduction in the slope (b) of the regression, relating total dry tissue weight (y) to shell length (x), for mussels ...
RegressionComputer analysisTransformationInteractive analysesA simple method of using an interactive computer and graphics monitor to develop the transformation to straighten any plot of two variables is presented. The inverse transformation may then be used to look at mean value function, confidence limits...
M. (1987) The application of the annealing algorithm to the construction of exact optimal designs for linear-regression models.Technometrics,29, 439–447. Google Scholar Johnson, D. S., Aragon, C. R., McGeoch, L. A. and Schevon, C. (1989) Optimization by simulated annealing: an ...
Furthermore, the performance of a support vector machine model has been compared with a heuristic technique, simulation model, linear regression model, neural network model, neuro-fuzzy model, autoregressive integrated moving average model, dynamic regression model, and transfer function model. It has ...
A linear regression fit is shown, as well as the equation and R2 values. Using the regression fit shown in FIG. 9, a generalization about the minimum segment length that avoids the compression-wrinkling failure for a laminate-segment of specific stiffness and rigidity if forced to bend when ...