Discriminant Analysis - Statpower

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Following Morrison (1983), suppose we indicate group membership with the dummy variable y i ⎧ N2 ⎪N + N = ⎨ ⎪ − N1 ⎪ ⎩N + N 1 2 1 2 ,(group 1) ,(group 2) (2) One may easily show (Morrison, 1983, p. 258) that the vector of estimated regression coefficients for predicting the y scores from the x variates is
( ) βˆ = cA −1 x −x (3) 1 2 where c = NN 1 2/ ( N1+ N2) [ −1 NN ( N N )]( x x )′ A ( x x ) 1 + / + − − 1 2 1 2 1 2 1 2 (4) The predicted y scores are ( ) y = βˆ ′ x − x (5) ˆi i
Page 1 and 2: Discriminant Analysis In discrimina
Page 3 and 4: Linear Discriminant Function - Two
Page 5: The linear discriminant function wa
Page 9 and 10: Recall that group 1 is associated w
Page 11 and 12: so ˆβ in Equation (9) can be repl
Page 13 and 14: Of course, we could generate a diff
Page 15 and 16: above function values will lead to
Page 17 and 18: Example. Morrison (1990, page 143)
Page 19 and 20: In this case, −1 S is given by
Page 21 and 22: Note that variables x 1 and x 3 do
Page 23 and 24: But first, let’s perform the anal
Page 25 and 26: Now, if we drop the two non-contrib

Discriminant Analysis - Statpower

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