Factorisation Theorem for Sufficiency

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Discussion Overview

The discussion revolves around the application of the Factorisation Theorem for Sufficiency in the context of likelihood functions. Participants explore how to identify sufficient statistics for parameters in different distributions, particularly focusing on when to set h(x) to 1 and the implications of this choice.

Discussion Character

  • Technical explanation
  • Conceptual clarification
  • Debate/contested

Main Points Raised

  • One participant expresses difficulty in applying the Factorisation Theorem to likelihood functions to find sufficient statistics.
  • Another participant notes that in the Poisson distribution, h(x) is set to 1/(x1!x2!) instead of 1, prompting a question about the conditions under which h(x) can be a constant.
  • It is suggested that h(x) should not depend on the parameter, but can be a constant or a function of x.
  • A later reply clarifies that the choice of h(x) being a constant or not is important for determining sufficiency.
  • Further questions arise regarding whether n, the sample size, needs to be treated as a sufficient statistic or if it is considered a constant.

Areas of Agreement / Disagreement

Participants generally agree on the principle that h(x) should not depend on the parameter, but there is uncertainty regarding specific cases and the treatment of n as a sufficient statistic.

Contextual Notes

Participants discuss various likelihood functions and their forms, indicating that the application of the theorem may depend on the specific characteristics of the distributions involved. There is no resolution on the treatment of n in relation to sufficiency.

Coolster7
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I'm having trouble with applying this theorem to likelihood functions in order to obtain a sufficiency statistic for the relevant variables.

_________________________________________________________________________________________

The factorisation theorem being:

Under certain regularity conditions;

T(X) is sufficient for θ ⇔ f(x|θ) = h(x)g(t(x),θ)

for some functions h and g.

__________________________________________________________________________________________

The main problem I'm having is when to allow h(x) = 1.

For example in the exponential distribution you get a likelihood function: f(x|θ) = θn(1-θ)\sum(xi) - n

you set h(x) =1 here and g(x) = (t,θ) = θn(1-θ)t - n where t = \sum(xi)

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However, in the Poisson distribution you get a likelihood function: 1/(x1!x2!) x e-2θθx1+x2.

here you set h(x) = 1/(x1!x2!) and not h(x) = 1.

Is this because h(x) has to be a constant or involving just x?

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So say for example you had a likelihood function:

f(x|θ) = xnσ-2ne-0.5nx2σ-2

using the Factorisation method would you let h(x) = 1 with g(x, σ) = f(x|θ)

and say x is sufficient for σ

OR would you let h(x) = xn and g(x, σ) = σ-2ne-0.5nx2σ-2

and say x is sufficient for σ

Note: Obviously there is the same outcome for the sufficiency statistic, but in a different problem this may not be the case.

Can anyone help me please?
 
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Coolster7 said:
However, in the Poisson distribution you get a likelihood function: 1/(x1!x2!) x e-2θθx1+x2.

here you set h(x) = 1/(x1!x2!) and not h(x) = 1.

Is this because h(x) has to be a constant or involving just x?

The idea is to have h(x) not depending on the parameter. Whether it's a constant or not.
 
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h6ss said:
The idea is to have h(x) not depending on the parameter. Whether it's a constant or not.

Ah I see, thanks for this I understand now.. it's simple really.
 
h6ss said:
The idea is to have h(x) not depending on the parameter. Whether it's a constant or not.

Actually just one more question. What about n? Would n need to be sufficient for the parameter or is it treated as constant/number?
 
Coolster7 said:
Actually just one more question. What about n? Would n need to be sufficient for the parameter or is it treated as constant/number?
Anyone?
 

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