He listed seven types of regression models as linear regression, logistic regression, polynomial regression, stepwise regression, ridge regression, Lasso regression, elastic net regression.
By use of the same method for each series of infected RBCs at different stages and percentages of parasitemia, a range of optimum regression models were obtained for both lipid and Hz band ranges with the minimum number of factors and highest model fitness.
Here, we consider two methodologically distinct approaches; a GWAMA-based polygenic score, which is entirely based on results from the literature, and a range of penalized regression models with and without pre-filtering of markers, which are entirely based on estimation procedures using our own data.
For two-dimensional dose response curves, a range of nonlinear regression models (e.g., power and logistic models) has been developed to model the sole source data, as opposed to the multi-source data described above.
SEMs constitute a broad range of multivariate regression models that allow complex dependencies among multiple predictors and outcome variables and are widely used in economics, sociology and psychology (Pugesek et al., 2003; Rabe-Hesketh et al., 2004; Skrondal and Rabe-Hesketh, 2004).
The data are outlined and summarized in section 4. Section 5 reports the results of regression modeling and a range of feasible robustness checks, while section 6 sums up.
Using an extensive commercial data set, we develop a methodology for the extraction of exposure metrics from location trajectories and estimate a range of multivariate logistic regression models in a case-control study design.
Within this displacement range, the regression model was linear.
We assessed the relative importance of the phylogenetic vs. ecological and geographic factors in determining the size of the geographical range through the use of multiple regression models.
This means that the functional form of empirical regression models in the range of effects below this statistical detection limit can neither be rejected nor confirmed empirically.
Our ANN approach extends the predictive range of the linear regression model, by replacing the identity functions with nonlinear activation functions, and it appears more suitable to describe complicated systems.
