I'm not sure what you're doing with that ratio.
You need the conditional density.
Instead use the factorization theorem.
Sufficient statistic - Wikipedia, the free encyclopedia
Show that the product of the sample observations is a sufficient statistic for theta if the random sample is taken from a sample with parameters alpha = theta and beta = 6.
So I need to make sure that is a sufficient estimator for theta, which is true if:
does not depend on theta.
But I keep getting getting 1 for the ratio. Am I correct?
I'm not sure what you're doing with that ratio.
You need the conditional density.
Instead use the factorization theorem.
Sufficient statistic - Wikipedia, the free encyclopedia
The theorem I'm using is:
and if h(x1,...,xn) doesn't depend on theta, then it's an sufficient estimator.
It looks roughly the same as the formula given on the wikipedia entry for sufficient estimators(By Hoggs and Craig which is the textbook I'm using right now):
I get h(x1,...,xn)=1 and I'm wondering if this is correct.
NO, its that part that is stuck with theta that is suff.
The product is suff for theta because of the theta in the expoenent of the product
that one over the product part that can go into h function (it's garbage)
The key term is
The is garbage that can go into h
Fisher's factorization theorem or factorization criterion provides a convenient characterization of a sufficient statistic. If the probability density function is ƒθ(x), then T is sufficient for θ if and only if functions g and h can be found such that
i.e. the density ƒ can be factored into a product such that one factor, h, does not depend on θ and the other factor, which does depend on θ, depends on x only through T(x).
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THE SUFF STAT IS THE PART WITH X's that cannot be separated from the parameter (theta).
The h function is ONLY a function of just x's
WHILE g is a function of theta and THE SUFF STAT.