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Nonasymptotic Analysis of the Lawley–Hotelling Statistic for High-Dimensional Data

We consider the General Linear Model (GLM) which includes multivariate analysis of variance (MANOVA) and multiple linear regression as special cases. In practice, there are several widely used criteria for GLM: Wilks’ lambda, Bartlett–Nanda–Pillai test, Lawley–Hotelling test, and Roy maximum root test. Limiting distributions for the first three mentioned tests are known under different asymptotic settings. In the present paper, we obtain computable error bounds for the normal approximation of the Lawley–Hotelling statistic when the dimension grows proportionally to the sample size. This result enables us to get more precise calculations of the p-values in applications of multivariate analysis. In practice, more and more often analysts encounter situations where the number of factors is large and comparable with the sample size. Examples include medicine, biology (i.e., DNA microarray studies), and finance.

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Correspondence to A. A. Lipatiev.

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Translated from Zapiski Nauchnykh Seminarov POMI, Vol. 486, 2019, pp. 178–189.

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Lipatiev, A.A., Ulyanov, V.V. Nonasymptotic Analysis of the Lawley–Hotelling Statistic for High-Dimensional Data. J Math Sci 258, 859–866 (2021). https://doi.org/10.1007/s10958-021-05595-w

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