{[ promptMessage ]}

Bookmark it

{[ promptMessage ]}

# lec2 - These last two results can be demonstrated as...

This preview shows pages 1–5. Sign up to view the full content.

These last two results can be demonstrated as follows: SS A = n X i y i · - ¯ y ·· ) 2 since ¯ y i · = μ + a i + ¯ e i · and ¯ y ·· = μ + ¯ a · + ¯ e ·· , it follows that SS A = n X i ( a i + ¯ e i · - ¯ a · - ¯ e ·· ) 2 = n X i ( γ i - ¯ γ · ) 2 where γ i = a i + ¯ e i · , ¯ γ · = ¯ a · + ¯ e ·· . Notice that γ 1 , . . . , γ a iid N (0 , σ 2 a + σ 2 /n ). Therefore, γ 1 / p σ 2 a + σ 2 /n, . . . , γ a / p σ 2 a + σ 2 /n iid N (0 , 1). It follows that SS A n ( σ 2 a + σ 2 /n ) = X i ( γ i - ¯ γ · ) 2 ( σ 2 a + σ 2 /n ) = X i γ i p σ 2 a + σ 2 /n - ¯ γ · p σ 2 a + σ 2 /n ! 2 is a sum of a squared deviations from the mean in standard normal random variables (the γ i σ 2 a + σ 2 /n ’s). Therefore, SS A 2 a + σ 2 χ 2 ( a - 1) . And since the expected value of a χ 2 (d . f . ) random variable is d . f . , we have E SS A 2 a + σ 2 = a - 1 E SS A a - 1 = 2 a + σ 2 . 101

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Under H 0 : σ 2 a = 0, F = MS A MS E F ( a - 1 , N - a ) and we reject H 0 if F > F α ( a - 1 , N - a ). ANOVA Table: Source of Sum of d.f. Mean E( MS ) F Variation Squares Squares Treatments SS A a - 1 MS A 2 a + σ 2 MS A MS E Error SS E N - a MS E σ 2 Total SS T N - 1 For an unbalanced design, replace n with ( N - i n 2 i /N ) / ( a - 1) in the above ANOVA table. 102
Estimation of Variance Components Since MS E is an unbiased estimators of its expected value σ 2 , we use ˆ σ 2 = MS E to estimate σ 2 . In addition, E MS A - MS E n = 2 a + σ 2 - σ 2 n = σ 2 a , so we use ˆ σ 2 a = MS A - MS E n to estimate σ 2 a . The validity of this estimation procedure isn’t dependent on normal- ity assumptions (on a i s and e ij s). In addition, it can be shown that (under certain assumptions) the proposed estimators are optimal in a certain sense. Occasionally, MS A < MS E . In such a case we will get ˆ σ 2 a < 0. Since a negative estimate of a variance component makes no sense, in this case ˆ σ 2 a is set equal to 0. 103

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
Confidence Intervals for Variance Components: Since SS E σ 2 χ 2 ( N - a ) it must be true that Pr χ 2 1 - α/ 2 ( N - a ) SS E σ 2 χ 2 α/ 2 ( N - a ) = 1 - α Inverting all three terms in the inequality just reverses the signs to ’s: Pr 1 χ 2 1 - α/ 2 ( N - a ) σ 2 SS E 1 χ 2 α/ 2 ( N - a ) ! = 1 - α Pr SS E χ 2 1 - α/ 2 ( N - a ) σ 2 SS E χ 2 α/ 2 ( N - a ) ! = 1 - α Therefore, a 100(1 - α )% CI for σ 2 is SS E χ 2 α/ 2 ( N - a ) , SS E χ 2 1 - α/ 2 ( N - a ) ! . It turns out that it is a good bit more complicated to derive a confidence interval for σ 2 a . However, we can more easily find exact CIs for the intra- class correlation coefficient ρ = σ 2 a σ 2 a + σ 2 and for the ratio of the variance components: θ = σ 2 a σ 2 . Both of these parameters have useful interpretations: ρ represents the proportion of the total variance that is the result of differences between treatments; θ represents the ratio of the between treatment variance to the within-treatment or error variance.
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}