c1s2 - 1.2 Decision Theory Usually in statistics instead of...

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Unformatted text preview: March 18, 2003 1.2 Decision Theory . Usually in statistics, instead of just two possible probability distributions P, Q, as in the last section, there is an infinite family P of such distributions, defined on a sample space , which is a measurable space ( X, B ), in other words a set X together with a -algebra B of subsets of X . As noted previously, if X is a subset of a Euclidean space, then B will usually be the -algebra of Borel subsets of X . If X is a countable set, then B will usually be the -algebra of all subsets of X (if also X R k , then all its subsets are in fact Borel sets). A probability measure on B will be called a law . The family P of laws on ( X, B ) is usually written as { P , } , where is called a parameter space . For example, if P is the set of all normal measures N ( , 2 ) for R and > 0, we can take = ( , ) or ( , 2 ) where in either case is the open upper half-plane, that is, the set of all ( t, u ) R 2 such that u > 0. We assume that the function P from to laws on B is one-to-one, in other words P = P whenever = in . So the sets P and are in 1-1 correspondence and any structure on one can be taken over to the other. We also assume given a -algebra T of subsets of . Most often will be a subset of some Euclidean space and T the family of Borel subsets of . The family { P , } will be called measurable on ( , T ) if and only if for each B B , the function P ( B ) is measurable on . If is finite or countable, then (as with sample spaces) T will usually be taken to be the collection of all its subsets. In that case the family { P , } is always measurable. An observation will be a point x of X . Given x , the statistician tries to make inferences about , such as estimating by a function ( x ). For example, if X = R n and P = N ( , 1) n , so x = ( X 1 , . . . , X n ) where the X i are i.i.d. with distribution N ( , 1), then ( x ) = X := ( X 1 + X n ) /n is the classical estimator of . In decision theory, there is also a measurable space ( D, S ), called the decision space . A measurable function d ( ) from X into D is called a decision rule . Such a rule says that if x is observed, then action d ( x ) should be taken. One possible decision space D would be the set of all d for , where d is the decision (estimate) that is the true value of the parameter. Or, if we just have a set P of laws, then d P would be the decision that P is the true law. Thus in the last section we had P = { P, Q } and for non-randomized tests, D = { d P , d Q } . There, a decision rule is equivalent to a measurable subset of X , which was taken to be the set where the decision will be d Q . For randomized rules, still for P = { P, Q } , the decision space D can be taken as the interval d 1, where d ( x ) is the probability that Q will be chosen if...
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This note was uploaded on 09/19/2011 for the course MATH 111 taught by Professor Jj during the Spring '09 term at AIU Online.

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c1s2 - 1.2 Decision Theory Usually in statistics instead of...

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