This becomes an enabler for large-scale MVA studies: while a Monte Carlo simulation across the design space is difficult with physics-based codes, it becomes trivial when evaluating surrogate models, which often take the form of response-surface equations.
Rather than discarding the whole data point, it is common to "fill in" values for the missing components, a process called "imputation".
Anderson's 1958 textbook, An Introduction to Multivariate Statistical Analysis,[7] educated a generation of theorists and applied statisticians; Anderson's book emphasizes hypothesis testing via likelihood ratio tests and the properties of power functions: admissibility, unbiasedness and monotonicity.
[8][9] MVA was formerly discussed solely in the context of statistical theories, due to the size and complexity of underlying datasets and its high computational consumption.
With the dramatic growth of computational power, MVA now plays an increasingly important role in data analysis and has wide application in Omics fields.