Industrial data analytics for diagnosis and prognosis : a random effects modelling approach

Introduction to data visualization and characterization -- Random vectors and the multivariate normal distribution -- Explaining covariance structure : principal components -- Linear model for numerical and categorical response variables -- Linear mixed effects model -- Diagnosis of variation source using PCA -- Diagnosis of variation sources through random effects estimation -- Analysis of system diagnosability -- Prognosis through mixed effects models for longitudinal data -- Prognosis using Gaussian process model -- Prognosis through mixed effects models for time-to-event data.