The two-group cross-validation classification accuracies of six algorithms (i.e., least squares, ridge regression, principal components, a common factor method, equal weighting, and logistic regression) were compared as a function of degree of validity concentration, group separation, and number of subjects. Therein, the findings of two previous studies were extended to the latter three methods...

Fitting quantitative structure-activity relationships (QSAR) requires different statistical methodologies and, to some degree, philosophies depending on the "shape" of the data matrix. When few features are used and there are many compounds, it is a reasonable expectation that good feature subset selection may be made and that nonlinearities and nonadditivities can be detected and diagnosed. Wh...

We introduce an efficient parallel implementation of a Kernel Ridge Regression solver, based on the Gaussian Belief Propagation algorithm (GaBP). Our approach can be easily used in Peer-to-Peer and grid environments, where there is no central authority that allocates work. Empirically, our solver has high accuracy in solving classification problems. We have tested our distributed implementation...

In this paper, regression analysis method i.e., traditional method and ridge regression is applied on the dataset collected for the prediction of compressive strength of concrete. When regression analysis is applied and data set is increased or decreased, the regression coefficients have drastically changed their values like the negative coefficients changed to positive or vice versa. So, tradi...

The paper aims to investigate the improvement that Ridge regression brings to the commercial real estate index estimation. The MIT transaction based index extends the use of the last model by Fisher, Gatzlaff, Geltner and Haurin (2003.) They address the sample selection bias and the noise effect of the transaction prices in small sample. To correct sample selection bias, MIT transaction index u...