Constrained linear discriminant rule via the Studentized classification statistic based on monotone missing data

• This paper provides an asymptotic expansion for the distribution of the Studentized linear discriminant function with k-step monotone missing training data. It turns out to be a certain generalization of the results derived by Anderson [1] and Shutoh and Seo [12]. Furthermore we also derive the cutoff point constrained by a conditional probability of misclassification using the idea of McLachlan [8]. Finally we perform Monte Carlo simulation to evaluate our results.

Subject headings

NATURVETENSKAP  -- Matematik (hsv//swe)

NATURAL SCIENCES  -- Mathematics (hsv//eng)

Keyword

Asymptotic expansion

Linear discriminant analysis

Monotone missing data

Probabilities of misclassification

Publication and Content Type

ref (subject category)

art (subject category)