This paper is concerned with computable error bounds for asymptotic approximations of the expected probabilities of misclassification (EPMC) of the quadratic discriminant function $Q$. A location and scale mixture expression for $Q$ is given as a special case of a general discriminant function including the linear and quadratic discriminant functions. Using the result, we provide computable error bounds for asymptotic approximations of the EPMC of $Q$ when both the sample size and the dimensionality are large. The bounds are numerically explored. Similar results are given for a quadratic discriminant function $Q_0$ when the covariance matrix is known.
The author is supported by Grant-in-aid for Science Research (C), 16K00047, 2016–2018.
"Computable error bounds for asymptotic approximations of the quadratic discriminant function." Hiroshima Math. J. 50 (3) 313 - 324, November 2020. https://doi.org/10.32917/hmj/1607396491