Faculty of Economics, University of Tokyo
Department of Statistics, University of Toronto
抄録
The problem of classifying a new observation vector into one of the two known groups distributed as multivariate normal with common covariance matrix is consid- ered. In this paper, we handle the situation that the dimension, p, of the observation vectors is less than the total number, N, of observation vectors from the two groups, but both p and N tend to in nity with the same order. Since the inverse of the sample covariance matrix is close to an ill condition in this situation, it may be better to replace it with the inverse of the ridge-type estimator of the covariance matrix in the linear discriminant analysis (LDA). The resulting rule is called the ridge-type linear discriminant analysis (RLDA). The second-order expansion of the expected probability of misclassi cation (EPMC) for RLDA is derived, and the second-order unbiased estimator of EMPC is given. These results not only provide the corresponding conclusions for LDA, but also clarify the condition that RLDA improves on LDA in terms of EPMC. Finally, the performances of the second-order approximation and the unbiased estimator are investigated by simulation.
内容記述
本文フィルはリンク先を参照のこと
雑誌名
Discussion paper series. CIRJE-F
巻
CIRJE-F-818
発行年
2011-09
書誌レコードID
AA11450569
フォーマット
application/pdf
日本十進分類法
335
出版者
日本経済国際共同センター
出版者別名
Center for International Research on the Japanese Economy
Asymptotic Expansion and Estimation of EPMC for Linear Classification Rules in High Dimension
