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Course by: Clayton Scott. E-mail the author

# The Fisher-Neyman Factorization Theorem

Module by: Clayton Scott, Robert Nowak. E-mail the authors

Determining a sufficient statistic directly from the definition can be a tedious process. The following result can simplify this process by allowing one to spot a sufficient statistic directly from the functional form of the density or mass function.

## Theorem 1: Fisher-Neyman Factorization Theorem

Let f θ x f θ x be the density or mass function for the random vector x x, parametrized by the vector θ θ. The statistic t=Tx t T x is sufficient for θ θ if and only if there exist functions ax a x (not depending on θ θ) and b θ t b θ t such that f θ x=axb θ t f θ x a x b θ t for all possible values of x x.

In an earlier example we computed a sufficient statistic for a binary communication source (independent Bernoulli trials) from the definition. Using the above result, this task becomes substantially easier.

## Example 1

### Bernoulli Trials Revisited

Suppose x n Bernoulliθ x n Bernoulli θ are IID,   ,   n= 1 , , N    n n 1 , , N . Denote x= x 1 x n T x x 1 x n . Then

f θ x= n =1Nθ x n 1θ1 x n =θk1θNk=axb θ k f θ x n 1 N θ x n 1 θ 1 x n θ k 1 θ N k a x b θ k
(1)
where k= n =1N x n k n 1 N x n , ax=1 a x 1 , and b θ k=θk1θNk b θ k θ k 1 θ N k . By the Fisher-Neyman factorization theorem, k k is sufficient for θ θ.

The next example illustrates the appliction of the theorem to a continuous random variable.

## Example 2

### Normal Data with Unknown Mean

Consider a normally distributed random sample x 1 , , x N 𝒩θ1 x 1 , , x N θ 1 , IID, where θθ is unknown. The joint pdf of x= x 1 x n T x x 1 x n is f θ x= n =1Nf θ x n =12πN2e-12 n =1N x n θ2 f θ x n 1 N f θ x n 1 2 N 2 -1 2 n 1 N x n θ 2 We would like to rewrite f θ x f θ x is the form of axb θ t a x b θ t , where dimt<N dim t N . At this point we require a trick-one that is commonly used when manipulating normal densities, and worth remembering. Define x-=1N n =1N x n x 1 N n 1 N x n , the sample mean. Then

f θ x=12πN2e-12 n =1N x n x-+x-θ2=12πN2e-12 n =1N x n x-2+2( x n x-)(x-θ)+x-θ2 f θ x 1 2 N 2 -1 2 n 1 N x n x x θ 2 1 2 N 2 -1 2 n 1 N x n x 2 2 x n x x θ x θ 2
(2)
Now observe
n =1N( x n x-)(x-θ)=(x-θ) n =1N x n x-=(x-θ)(x-x-)=0 n 1 N x n x x θ x θ n 1 N x n x x θ x x 0
(3)
so the middle term vanishes. We are left with f θ x=12πN2e-12 n =1N x n x-2e-12 n =1Nx-θ2 f θ x 1 2 N 2 -1 2 n 1 N x n x 2 -1 2 n 1 N x θ 2 where ax=12πN2e-12 n =1N x n x-2 a x 1 2 N 2 -1 2 n 1 N x n x 2 , b θ t=e-12 n =1Nx-θ2 b θ t -1 2 n 1 N x θ 2 , and t=x t x . Thus, the sample mean is a one-dimensional sufficient statistic for the mean.

## Proof of Theorem

First, suppose t=Tx t T x is sufficient for θ θ. By definition, f θ | Tx=t x f θ T x t x is independent of θ θ. Let f θ xt f θ x t denote the joint density or mass function for ( X , T ( X ) ) ( X , T ( X ) ) . Observe f θ x=f θ xt f θ x f θ x t . Then

f θ x=f θ xt=f θ | t xf θ t=axb θ t f θ x f θ x t f θ t x f θ t a x b θ t
(4)
where ax=f θ | t x a x f θ t x and b θ t=f θ t b θ t f θ t . We prove the reverse implication for the discrete case only. The continuous case follows a similar argument, but requires a bit more technical work (Scharf, pp.82; Kay, pp.127).

Suppose the probability mass function for xx can be written f θ x=axb θ x f θ x a x b θ x where t=Tx t T x . The probability mass function for tt is obtained by summing f θ xt f θ x t over all xx such that Tx=t T x t :

f θ t=Tx=tf θ xt=Tx=tf θ x=Tx=taxb θ t f θ t x T x t f θ x t x T x t f θ x x T x t a x b θ t
(5)
Therefore, the conditional mass function of xx, given tt, is
f θ | t x=f θ xtf θ t=f θ xf θ t=axTx=tax f θ t x f θ x t f θ t f θ x f θ t a x x T x t a x
(6)
This last expression does not depend on θθ, so tt is a sufficient statistic for θθ. This completes the proof.

### Remark:

From the proof, the Fisher-Neyman factorization gives us a formula for the conditional probability of xx given tt. In the discrete case we have fx| t =axTx=tax f t x a x x T x t a x An analogous formula holds for continuous random variables (Scharf, pp.82).

## Further Examples

The following exercises provide additional examples where the Fisher-Neyman factorization may be used to identify sufficient statistics.

### Exercise 1

#### Uniform Measurements

Suppose x 1 , , x N x 1 , , x N are independent and uniformly distributed on the interval θ 1 θ 2 θ 1 θ 2 . Find a sufficient statistic for θ= θ 1 θ 2 T θ θ 1 θ 2 .

##### Hint:
Express the likelihood f θ x f θ x in terms of indicator functions.

### Exercise 2

#### Poisson

Suppose x 1 , , x N x 1 , , x N are independent measurements of a Poisson random variable with intensity parameter θθ: f θ x=eθθxx!  ,   x= 0 , 1 , 2 ,    x x 0 , 1 , 2 , f θ x θ θ x x

##### 2.a)

Find a sufficient statistic tt for θθ.

##### 2.b)

What is the conditional probability mass function of xx, given tt, where x= x 1 x N T x x 1 x N ?

### Exercise 3

#### Normal with Unknown Mean and Variance

Consider x 1 , , x N 𝒩μσ2 x 1 , , x N μ σ 2 , IID, where θ 1 =μ θ 1 μ and θ 2 =σ2 θ 2 σ 2 are both unknown. Find a sufficient statistic for θ= θ 1 θ 2 T θ θ 1 θ 2 .

##### Hint:
Use the same trick as in Example 2.

## References

1. L. Scharf. (1991). Statistical Signal Processing. Addison-Wesley.
2. Kay. (1993). Estimation Theory. Prentice Hall.

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