In many high dimensional problems, the dependence structure among the variables can be quite complex. An appropriate use of the regularization techniques coupled with other classical statistical methods can often improve estimation and prediction accuracy and facilitate model interpretation, by seeking a parsimonious model representation that involves only the subset of revelent variables. We p...

In our discussion of linear models relating rCBF to design factors and gCBF, we have not considered the computational problems of simultaneously fitting the model for thousands of voxels.32 Viewing the problem from a multivariate perspective provides efficient computation, and offers some insight into the problem. In this section we demonstrate the relationship between the simultaneous general ...

This paper presents a method for distributed multivariate regression using waveletbased Collective Data Mining (CDM). The method seamlessly blends machine learning and the theory of communication with the statistical methods employed in parametric multivariate regression to provide an effective data mining technique for use in a distributed data and computation environment. The technique is app...

As in many areas of biostatistics, oncological problems often have multivariate predictors. While assuming a linear additive model is convenient and straightforward, it is often not satisfactory when the relation between the outcome measure and the predictors is either nonlinear or nonadditive. In addition, when the number of predictors becomes (much) larger than the number of independent obser...

abstract: background and purpose: the survival rate of gastric cancer patients has significantly increased during the last decades because of the advances in this type of cancer treatment. the purpose of the current study was to examine the survival levels of gastric cancer through weibull regression and its affective causes. materials and methods: in this research, the records and personal inf...