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Unformatted text preview: Maximum likelihood estimators and least squares November 11, 2010 1 Maximum likelihood estimators A maximum likelihood estimate for some hidden parameter λ (or parameters, plural) of some probability distribution is a number ˆ λ computed from an i.i.d. sample X 1 , ..., X n from the given distribution that maximizes something called the “likelihood function”. Suppose that the distribution in question is governed by a pdf f ( x ; λ 1 , ..., λ k ), where the λ i ’s are all hidden parameters. The likelihood function associated to the sample is just L ( X 1 , ..., X n ) = n productdisplay i =1 f ( X i ; λ 1 , ..., λ k ) . For example, if the distribution is N ( μ, σ 2 ), then L ( X 1 , ..., X n ; ˆ μ, ˆ σ 2 ) = 1 (2 π ) n/ 2 ˆ σ n exp parenleftbigg 1 2ˆ σ 2 ( ( X 1 ˆ μ ) 2 + ··· + ( X n ˆ μ ) 2 ) parenrightbigg . (1) Note that I am using ˆ μ and ˆ σ 2 to indicate that these are variable (and also to set up the language of estimators). Why should one expect a maximum likelihood esimate for some parameter to be a “good estimate”? Well, what the likelihood function is measuring is how likely ( X 1 , ..., X n ) is to have come from the distribution assuming particular values for the hidden parameters; the more likely this is, the closer one would think that those particular choices for hidden parameters are to the true values. Let’s see two examples: 1 Example 1. Suppose that X 1 , ..., X n are generated from a normal distribu tion having hidden mean μ and variance σ 2 . Compute a MLE for μ from the sample. Solution. As we said above, the likelihood function in this case is given by (1). It is obvious that to maximize L as a function of ˆ μ and ˆ σ 2 we must minimize n summationdisplay i =1 ( X i ˆ μ ) 2 as a function of ˆ μ . Upon taking a derivative with respect to ˆ μ and setting it to 0, we find that...
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 Spring '08
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 Statistics, Probability

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