In statistical theory, the field of high-dimensional statistics studies data whose dimension is larger (relative to the number of datapoints) than typically considered in classical multivariate analysis. The area arose owing to the emergence of many modern data sets in which the dimension of the data vectors may be comparable to, or even larger than, the sample size, so that justification for the use of traditional techniques, often based on asymptotic arguments with the dimension held fixed as the sample size increased, was lacking.[1][2]
There are several notions of high-dimensional analysis of statistical methods including:
- Non-asymptotic results which apply for finite (number of data points and dimension size, respectively).
- Kolmogorov asymptotics which studies the asymptotic behavior where the ratio is converges to a specific finite value.[3]
- ^ Lederer, Johannes (2022). Fundamentals of High-Dimensional Statistics: With Exercises and R labs. Springer Textbooks in Statistics. doi:10.1017/9781108627771. ISBN 9781108498029. S2CID 128095693.
- ^ Wainwright, Martin J. (2019). High-Dimensional Statistics: A Non-Asymptotic Viewpoint. Cambridge University Press. doi:10.1017/9781108627771. ISBN 9781108498029. S2CID 128095693.
- ^ Wainwright MJ. High-Dimensional Statistics: A Non-Asymptotic Viewpoint. Cambridge: Cambridge University Press; 2019. doi:10.1017/9781108627771