Page 292 - Fundamentals of Probability and Statistics for Engineers
P. 292

Parameter Estimation                                            275
           9.2.4  SUFFICIENCY


           Let  X 1 , X 2 ,..., X n  be a  sample of a  population  X  the distribution  of which

           depends on unknown parameter . If Y ˆ h X 1 , X 2 , ... , X n )isa statisticsuch
           that, for any other statistic
                                   Z ˆ g…X 1 ; X 2 ; ... ; X n †;

                                                   =
           the conditional distribution of Z , given that Y  y does not depend on  , then
           Y  is called a  sufficient statistic for  . If also EfYgˆ   , then Y  is said  to be a

           sufficient estimator for  .
             In words, the definition for sufficiency states that, if Y  is a sufficient statistic
           for  ,  all  sample  information  concerning    is  contained  in  Y .  A  sufficient

           statistic is thus of interest in that if it can be found for a parameter then an
           estimator based on this statistic is able to make use of all the information that
           the sample contains regarding the value of the unknown parameter. Moreover,
           an important property of a sufficient estimator is that, starting with any
           unbiased estimator of a parameter    that is not a function of the sufficient
           estimator, it is possible to find an unbiased estimator based on the sufficient
           statistic that has a variance smaller than that of the initial estimator. Sufficient
           estimators thus have variances that are smaller than any other unbiased esti-
           mators that do not depend on sufficient statistics.
             If a sufficient statistic for a parameter    exists, Theorem 9.4, stated here
           without proof, provides an easy way of finding it.



















             Theorem 9.4: Fisher–Neyman factorization criterion. Let


                                   Y ˆ h X 1 , X 2 , ... , X n )
           be a  statistic based  on  a  sample of size n.  Then  Y  is a  sufficient  statistic for
              if  and  only  if  the  joint  probability  density  function  of  X 1 , X 2 , . ..,  and
           X n , f X  x 1 ;  )     f X  x n ;  ),  can be factorized in the form
                         n
                        Y
                           f X …x j ;  †ˆ g 1 ‰h…x 1 ; ... ; x n †; Šg 2 …x 1 ; ... ; x n †:  …9:49†
                        jˆ1
           If X is discrete, we have
                         n
                        Y
                           p X …x j ;  †ˆ g 1 ‰h…x 1 ; ... ; x n †; Šg 2 …x 1 ; ... ; x n †:  …9:50†
                        jˆ1
             The sufficiency of the factorization criterion was first pointed out by Fisher
           (1922). Neyman (1935) showed that it is also necessary.
                                                                            TLFeBOOK
   287   288   289   290   291   292   293   294   295   296   297