Background: Regarding the increased risk of developing type 2 diabetes in pre-diabetic people, identifying pre-diabetes and determining of its risk factors seems so necessary. In this study, it is aimed to compare ordinary logistic regression and robust logistic regression models in modeling pre-diabetes risk factors. Methods: This is a cross-sectional study and conducted on 6460 people, over ...

the objective of this study was to investigate the effect of principal components (pc) of the regression coefficients (co)variance matrix on the lactation curve, and on the formation of selection index to modify the curve, based on these pc’s, and as well to estimate additive genetic and phenotypic variances and heritability of the constructed selection index. lactation period of 301 d (5-305) ...

It is well-known that Kernel Based Regression (KBR) with a least squares loss has some undesirable properties from robustness point of view. KBR with more robust loss functions, e.g. Huber or logistic losses, often give rise to more complicated computations and optimization problems. In classical statistics, robustness is improved by reweighting the original estimate. We study reweighting the K...

We review a recent neural implementation of Canonical Correlation Analysis and show, using ideas suggested by Ridge Regression, how to make the algorithm robust. The network is shown to operate on data sets which exhibit multicollinearity. We develop a second model which not only performs as well on multicollinear data but also on general data sets. This model allows us to vary a single paramet...

The key method in this thesis is least squares support vector machines (LSSVM), a class of kernel based learning methods that fits within the penalized modelling paradigm. Primary goals of the LS-SVM models are regression and classification. Although local methods (kernel methods) focus directly on estimating the function at a point, they face problems in high dimensions. Therefore, one can gua...