process capability indices show the ability of a process to produce products according to the pre-specified requirements. since final quality characteristics of a product are usually interrelated to its previous amounts in earlier workstations, one need to model and consider the relationship among them to assess the process ca-pability properly. hence, conducting process capability analysis in ...

As the molecular marker density grows, there is a strong need in both genome-wide association studies and genomic selection to fit models with a large number of parameters. Here we present a computationally efficient generalized ridge regression (RR) algorithm for situations in which the number of parameters largely exceeds the number of observations. The computationally demanding parts of the ...

In ridge regression the estimation of the ridge parameter is an important issue. This paper generalizes some methods for estimating the ridge parameter for probit ridge regression (PRR) model based on the work of Kibria et al. (2011). The performance of these new estimators are judged by calculating the mean square error (MSE) using Monte Carlo simulations. In the design of the experiment we ch...

The present study was conducted to predict survival time in patients with diffuse large B-cell lymphoma, DLBCL, based on microarray data using Cox regression model combined with seven dimension reduction methods. This historical cohort included 2042 gene expression measurements from 40 patients with DLBCL. In order to predict survival, a combination of Cox regression model was used with seven m...

This paper deals with ridge estimation of fuzzy nonparametric regression models using triangular fuzzy numbers. This estimation method is obtained by implementing ridge regression learning algorithm in the Lagrangian dual space. The distance measure for fuzzy numbers that suggested by Diamond is used and the local linear smoothing technique with the crossvalidation procedure for selecting the o...

We compare the risk of ridge regression to a simple variant of ordinary least squares, in which one simply projects the data onto a finite dimensional subspace (as specified by a principal component analysis) and then performs an ordinary (un-regularized) least squares regression in this subspace. This note shows that the risk of this ordinary least squares method (PCA-OLS) is within a constant...