The bias of Fisher's linear discriminant function when the variancies are not equal

Terence J. O'Neill

Statistics & Probability Letters, 1992, vol. 14, issue 3, 205-210

Abstract: Since Fisher's linear discriminant rule (FLDR) is the most widely used classification rule, its behaviour under non-standard situations is of great interest. This paper gives an expansion for the mean of FLDR when the equal variance matrices assumption is violated. The expansion has a particularly simple form for proportional covariance matrices.

Keywords: Fisher's; linear; discriminant; rule; proportional; covariance; matrices (search for similar items in EconPapers)
Date: 1992
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