sta 2006 0708_chapter_12

sta 2006 0708_chapter_12 - STA 2006 MLE Examples(Section...

Info iconThis preview shows pages 1–2. Sign up to view the full content.

View Full Document Right Arrow Icon
MLE: Examples (Section 6.14) 2007-2008 Term II 1 Fisher Information Denote - E [ { log( f ( X i ; θ )) } 00 ] by I ( θ ). This quantity is called the Fisher Information . It measures the average curvature of the log-density at the true parameter value. Also, it is the inverse of the asymptotic variance of n ˆ θ . Consider the asymptotic variance of ˆ θ as V ar ( ˆ θ ) 1 n × 1 I ( θ ) . (1) Since ˆ θ is consistent for θ , Equation (1) can be viewed as a measure of the estimation error. In fact, that consists of 2 components: 1. Sample size: 1 /n . The larger the better. 2. Model: If the model contains good amount of ”Information” at the true parameter value, the estimation error should be small because of 1 /I ( θ ). Example 1: Suppose X 1 ,...,X n i.i.d. Bernoulli( p ). Then, f ( X i ; p ) = p X i (1 - p ) 1 - X i log( f ( X i ; p )) = X i log( p ) + (1 - X i ) log(1 - p ) . Therefore, { log( f ( X i ; p )) } 0 = d dp log( f ( X i ; p )) = X i p - 1 - X i 1 - p { log( f ( X i ; p )) } 00 = d 2 dp 2 log( f ( X i ; p )) = - X i p 2 - 1 - X i (1 - p ) 2 and I ( p ) = - E [ { log( f ( X i ; p )) } 00 ] = E [ X i p 2 + 1 - X i (1 - p ) 2
Background image of page 1

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

View Full DocumentRight Arrow Icon
Image of page 2
This is the end of the preview. Sign up to access the rest of the document.

This note was uploaded on 06/05/2011 for the course STATISTICS 2006 taught by Professor Ho during the Spring '11 term at CUHK.

Page1 / 5

sta 2006 0708_chapter_12 - STA 2006 MLE Examples(Section...

This preview shows document pages 1 - 2. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online