Functional Data Analysis
Functional Data Analysis is a scholarly work by Hans-Georg Müller, published in 2016 in ''Annual Review of Statistics and Its Application''. The main subjects of the publication include sensory analysis, multicollinearity, machine learning, data mining, artificial intelligence, functional principal component analysis, covariance, cluster analysis, computer science, chemometrics, algorithmic stability, pattern recognition, functional data analysis, mathematics, dimensionality reduction, principal component analysis, and dynamic time warping. The paper provides an overview of FDA, starting with simple statistical notions such as mean and covariance functions, then covering some core techniques, the most popular of which is functional principal component analysis (FPCA).