note6a - STAT5044: Regression and Anova Inyoung Kim 1 / 49...

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Unformatted text preview: STAT5044: Regression and Anova Inyoung Kim 1 / 49 Outline 1 How to check assumptions 2 / 49 Assumption Linearity: scatter plot, residual plot Randomness: Run test, Durbin-Watson test when the data can be arranged in time order. Constant variance: scatter plot, residual plot (ABS-residual plot); Brown-Forsythe test, Breusch-Pagan Test Normality of error: Box-plot, histogram, normal probability plot; Shapiro-Wilks test, Kolmogorov-Smirnov, Anderson-Darling Remark: Normality probability plot provides no information if the assumption of linearity and/or constant variance are violated 3 / 49 Influential point Combination of large absolute residual and high leverage ( h ii ) Leverage: diagonal value of Hat matrix ( H ) H = h 11 h 12 h 1 n h 21 h 22 h 1 n . . . . . . . . . . . . h n 1 h n 2 h nn High leverage large h ii 4 / 49 Residual Three types: Ordinary r : r i = y- y , where E ( r i ) = 0 and var ( r i ) = ( 1- h ii ) 2 Standardized: r i 1- h ii Studendized (or Jackknife): r i ( i ) 1- h ii t n- 2 where, 2 ( i ) = ( j r 2 j ( i ) ) / ( n- p- 1 ) and (p+1) is the number of parameter h ii is the leverage which is the diagonal value of Hat matrix. r j ( i ) = y j- y j ( i ) = y j- ( ( i ) + 1 ( i ) x j ) 5 / 49 Properties of residuals Sum to zero: r i = Are not independent 6 / 49 Residual Jackknife r i ( i ) = y i- y i ( i ) N ( , 2 1- h ii ) where the subindex (i) indicate that estimate without point i. residual for y i computed using regression without y i then scaling. Studendized residual: r i ( i ) q var ( r i ( i ) ) r i ( i ) = y i- y i ( i ) = y i- [ ( i ) + 1 ( i ) x i ] 7 / 49 Studendized residual r i ( i ) q var ( r i ( i ) ) = r i ( i ) 1- h ii by Fact 1 and 2. Fact 1: r i ( i ) = r i 1- h ii Fact 2: r 2 i ( i ) = ( n- p ) 2- r 2 i 1- h ii ( i ) = ( n- p ) 2- r 2 i 1- h ii n- p- 1 8 / 49 Residual Using Fact1 r i ( i ) = r i 1- h ii , we have Var ( r i ( i ) ) = Var ( r i ) ( 1- h ii ) 2 = 2 1- h ii . But 2 is unknown. We use 2 ( i ) r i ( i ) = Y i- Y i ( i ) r i ( i ) = r i 1- h ii 9 / 49 Residual Studendized residual r i 1- h ii r 2 ( i ) 1- h ii = r i q 2 ( i ) ( 1- h ii ) 2 ( i ) = j r 2 j ( i ) n- p- 1 where j r 2 j ( i ) = ( n- p ) 2- r 2 i 1- h ii NOTE: large residual if | r j ( i ) | > 3 An expression for the distribution of the standardized residuals was obtained (Weisberg, 1985). 10 / 49 Studendized residual r i ( i ) q var ( r i ( i ) ) = r i ( i ) 1- h ii t n- p- 1 You dont need to know how to prove this in our class! (beyond our class scope) 11 / 49 Comparison with standardized residual Standardized residual: r i- p var ( r i ) = r i- p 2 ( 1- h ii ) r i p ( 1- h ii ) 2 If one has outliers with large absolute residual, then 2 may not be a good measurement.be a good measurement....
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This note was uploaded on 01/02/2012 for the course STAT 5044` taught by Professor Staff during the Fall '11 term at Virginia Tech.

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note6a - STAT5044: Regression and Anova Inyoung Kim 1 / 49...

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