Unformatted text preview: pdf, their joint pdf is Q n i =1 f ( x i ; θ ). This is the likelihood function. – Step 2 : We want to maximize the likelihood function with respect to θ . This is usually accomplished by maximized the loglikelihood function (because the algebra and calculus necessary to solve the problem are easier). – Step 3 : The value of θ which maximizes the (log)likelihood function is the MLE ˆ θ . – Step 4 : Given values from a random sample, x 1 , x 2 , . . . , x n , plug in the values to ﬁnd the point estimate....
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 Spring '08
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 Statistics, Probability, Estimation theory, Likelihood function, 1 K, random sample X1, Emily King

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