Unformatted text preview: f ( x ; θ ) = θ x θ – 1 , 0 < x < 1, where θ > 0. a) Find the sufficient statistic Y for θ . b) Show that the maximum likelihood estimator for θ is a function of Y . c) Argue that & ˆ is also sufficient for θ . 6. a) 7.2.4 b) 7.2.7 7. a) 7.2.6 b) 7.2.8 _________________________________________________________________________ If you are registered for 4 credit hours: ( to be handed in separately ) 8. 5.7.9...
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 Spring '08
 Monrad
 Normal Distribution, probability density function, Estimation theory, Likelihood function, maximum likelihood estimator, Bias of an estimator

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