This paper focuses on Optimal Experimental Design to train a Projection Pursuit Regression (PPR) model used for fault detection. A novel experimental design method, referred to as Gaussian Probability Design, is proposed and compared with the conventional Factorial Design. The Gaussian Probability Design automatically searches for the sparseness of the data, and adds pairs of training data on b...

A new class of two-level non-regular fractional factorial designs is defined. We call this class an affinely full-dimensional factorial design, meaning that design points in the design of this class are not contained in any affine hyperplane in the vector space over F2. The property of the indicator function for this class is also clarified. A fractional factorial design in this class has a des...

Characterized by simultaneous measurement of the effects of experimental factors and their interactions, the economic and efficient factorial design is well accepted in microarray studies. To date, the only statistical method for analyzing microarray data obtained using factorial design has been the analysis of variance (ANOVA) model which is a gene by gene approach and relies on multiple assum...

This study suggests two approaches to factorial invariance testing with multilevel data when the groups are at the within level: multilevel factor mixture model for known classes (ML FMM) and multilevel multiple indicators multiple causes model (ML MIMIC). The adequacy of the proposed approaches was investigated using Monte Carlo simulations. Additionally, the performance of different types of ...