An asymptotic expansion for the distribution of the linear discriminant function based on monotone missing data

作者：Shutoh Nobumichi^*

来源：Journal of Statistical Computation and Simulation, 2012, 82(2): 241-259.

DOI：10.1080/00949655.2011.633521

• discriminant analysis
• asymptotic expansion
• probabilities of misclassification
• asymptotic approximation
• monotone missing data

In this study, we derive an asymptotic expansion for the distribution of a linear discriminant function based on monotone missing training data. Asymptotic expansions play an important role in discriminant analysis in that they consider the probabilities of misclassification. We derive an asymptotic expansion for linear discrimination that is based on monotone missing training data. In other words, we derive a specific generalization of the results derived by Okamoto [An asymptotic expansion for the distribution of the linear discriminant function, Ann. Math. Statist. 34 (1963), pp. 1286-1301]. Finally, we evaluate the accuracy of our result by the Monte Carlo simulation.

• 出版日期2012