Background Various clinical (disease activity, seropositive RA etc.) and metabolic risk factors (Dkk1 have been associated with erosive rheumatoid arthritis (RA). However, such might be intertwined, multicollinearity reduce our ability to discern the individual contribution score. Principal component analysis (PCA) is statistical technique for reducing dataset’s dimension principal regression (...

ABSTRACT: Quantitative structure-activity relationship (QSAR) study on the piperidone-grafted mono- and bis-spirooxindole-hexahydropyrrolizines as potent butyrylcholinestrase (BuChE) inhibitors were carried out using statistical methods, molecular dynamics and molecular docking simulation. QSAR methodologies, including classification and regression tree (CART), multiple linear regression (MLR),...

To date, quality-related multivariate statistical methods are extensively used in process monitoring and have achieved admirable effects. However, most of them contain recursive processes, which result higher time complexity not suitable for increasingly complex industrial processes. Therefore, this paper embeds singular value decomposition (SVD) into the kernel principal component regression (...

Abstract Linear regression is a simple and widely used machine learning algorithm. It statistical approach for modeling the relationship between scalar variable one or more variables. In this paper, classification by principal component (CbPCR) strategy proposed. This depends on performing of each data class in terms its components. CbPCR formulation leads to new Regression Classification (LRC)...