Statistical techniques for extracting underlying information in network R&D

In this paper we discuss on some statistical techniques deals with blind source separation (BSS). We introduce some applications of these techniques for extracting hidden information lies in data gathered from network Research and Development (R&D) units. This kind of information can be applied extensively for value creation in network R&D units. We mainly concern on two useful techniques: 1. Independent Component Analysis (ICA) and, 2. Principal Component Analysis (PCA). These are two important multivariate solutions for this problem.

Keywords:

BSS , PCA , ICA , network R&D , Kano model , value creation.