100BHWS5

# 100BHWS5 - STAT 100B HWV Solution Problem 1 Prove that...

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Unformatted text preview: STAT 100B HWV Solution Problem 1: Prove that least squares is a special case of maximum likelihood, if we assume that the observational errors follow independent normal distributions. A: Assuming that Y i ∼ N ( α + βx i ,σ 2 ). The likelihood is L ( α,β,σ 2 ) = n Y i =1 1 √ 2 πσ 2 exp {- 1 2 σ 2 [ Y i- ( α + βx i )] 2 } = 1 (2 πσ 2 ) n/ 2 exp {- 1 2 σ 2 n X i =1 [ Y i- ( α + βx i )] 2 } . In order to maximize L ( α,β,σ 2 ), we need to minimize RSS ( α,β ) = ∑ n i =1 [ Y i- ( α + βx i )] 2 . This leads to the least squares. Problem 2: Suppose X 1 , ..., X n are independent observations from a mixture of normal distri- butions, λN ( μ 1 ,σ 2 1 ) + (1- λ ) N ( μ ,σ 2 ). Write down the likelihood function, and calculate the partial derivatives of the log-likelihood. Describe the EM algorithm for computing the parameter estimates. A: Let θ = ( λ,μ 1 ,σ 2 1 ,μ ,σ 2 ). Let f 1 ( x ; θ ) = 1 q 2 πσ 2 1 exp {- 1 2 σ 2 1 ( x- μ 1 ) 2 } , f ( x ; θ ) = 1 q 2 πσ 2 exp {- 1 2 σ 2 ( x- μ ) 2 } , and f ( x ; θ ) = λf 1 ( x ; θ ) + (1- λ ) f ( x ; θ ) . The likelihood is L ( θ ) = Q n i =1 f ( X i ; θ ), and the log-likelihood is l ( θ ) = ∑ n i =1 log f ( X i ; θ )....
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## This note was uploaded on 03/30/2011 for the course STAT 100B taught by Professor Wu during the Winter '11 term at UCLA.

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100BHWS5 - STAT 100B HWV Solution Problem 1 Prove that...

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