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Unformatted text preview: Section 7.6 We had a brief introduction/review to random sample (iid rvs), estimator, unbiased estimator, and consistent estimator. We now discuss a method of finding estimators which possess good properties. 1. Maximum Likelihood Estimation This method yields estimators that have many desirable properties; both finite as well as large sample properties. The basic idea to find an estimator ) ( x which is the most likely given the data ) ,..., ( 1 n X X X = . Example 1. Let ) 1 ( , X ~ B , Bernoulli population with density 1 , , ) 1 ( )  ( )  ( 1 x = = = = x x p x X P x where ) 1 ( = = X P . We want to estimate population proportion based on a random sample of size n . That is, n X X ,..., 1 are independent and identically distributed random variables. 1 For } 1 , { i x , we have ] ,..., [ 1 1 n n x X x X P = = ] [ . . . ] [ 1 1 n n x X P x X P = = = n n x x x x = 1 1 ) 1 ( ... ) 1 ( 1 1 = , ) 1 ( 1 1   n i n i x n x is called the joint density of...
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This note was uploaded on 07/25/2008 for the course STT 351 taught by Professor Palaniappan during the Summer '08 term at Michigan State University.
 Summer '08
 Palaniappan

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