1. STATISTICAL INFERENCE PART II SOME PROPERTIES OF ESTIMATORS

2. SOME PROPERTIES OF ESTIMATORS
θ: a parameter of interest; unknown
Previously, we found good(?) estimator(s) for θ or its function g(θ).
Goal:
Check how good are these estimator(s). Or are they good at all?
If more than one good estimator is available, which one is better?

3. SOME PROPERTIES OF ESTIMATORS
UNBIASED ESTIMATOR (UE): An estimator is an UE of the unknown parameter , if
Otherwise, it is a Biased Estimator of .
Bias of for estimating 
If is UE of ,

4. SOME PROPERTIES OF ESTIMATORS
ASYMPTOTICALLY UNBIASED ESTIMATOR (AUE): An estimator is an AUE of the unknown parameter , if

5. SOME PROPERTIES OF ESTIMATORS
CONSISTENT ESTIMATOR (CE): An estimator which converges in probability to an unknown parameter  for all  is called a CE of .
For large n, a CE tends to be closer to
the unknown population parameter.
MLEs are generally CEs.

6. EXAMPLE
For a r.s. of size n,
By WLLN,

7. MEAN SQUARED ERROR (MSE)
The Mean Square Error (MSE) of an estimator for estimating  is
If is smaller, is a better estimator
of .

8. MEAN SQUARED ERROR CONSISTENCY
is called mean squared error consistent (or consistent in quadratic mean) if E{ }2 0 as n.
Theorem: is consistent in MSE iff
Var( )0 as n.
If E{ }20 as n, is also a CE of .

9. EXAMPLES
X~Exp(), >0. For a r.s of size n, consider the following estimators of , and discuss their bias and consistency.

10. SUFFICIENT STATISTICS
X, f(x;), 
X1, X2,…,Xn
Y=U(X1, X2,…,Xn ) is a statistic.
A sufficient statistic, Y, is a statistic which contains all the information for the estimation of .

11. SUFFICIENT STATISTICS
Given the value of Y, the sample contains no further information for the estimation of .
Y is a sufficient statistic (ss) for  if the conditional distribution h(x1,x2,…,xn|y) does not depend on  for every given Y=y.
A ss for  is not unique:
If Y is a ss for , then any 1-1 transformation of Y, say Y1=fn(Y) is also a ss for .

12. SUFFICIENT STATISTICS
The conditional distribution of sample rvs given the value of y of Y, is defined as
If Y is a ss for , then
Not depend on  for every given y.
ss for 
may include y or constant.
Also, the conditional range of Xi given y not depend on .

13. SUFFICIENT STATISTICS
EXAMPLE: X~Ber(p). For a r.s. of size n, show that is a ss for p.

14. SUFFICIENT STATISTICS
Neyman’s Factorization Theorem: Y is a ss for  iff
The likelihood function
Does not depend on xi
except through y
Not depend on  (also in the range of xi.)
where k1 and k2 are non-negative functions.

15. EXAMPLES
1. X~Ber(p). For a r.s. of size n, find a ss for p if exists.

16. EXAMPLES
2. X~Beta(θ,2). For a r.s. of size n, find a ss for θ.

17. SUFFICIENT STATISTICS
A ss, that reduces the dimension, may not exist.
Jointly ss (Y1,Y2,…,Yk ) may be needed. Example: Example in Bain and Engelhardt (page 342 in 2nd edition), X(1) and X(n) are jointly ss for 
If the MLE of  exists and unique and if a ss for  exists, then MLE is a function of a ss for .

18. EXAMPLE
X~N(,2). For a r.s. of size n, find jss for  and 2.

19. MINIMAL SUFFICIENT STATISTICS
If is a ss for θ, then,
is also a ss
for θ. But, the first one does a better job in data reduction. A minimal ss achieves the greatest possible reduction.

20. MINIMAL SUFFICIENT STATISTICS
A ss T(X) is called minimal ss if, for any other ss T’(X), T(x) is a function of T’(x).
THEOREM: Let f(x;) be the pmf or pdf of a sample X1, X2,…,Xn. Suppose there exist a function T(x) such that, for two sample points x1,x2,…,xn and y1,y2,…,yn, the ratio
is constant with respect to  iff T(x)=T(y). Then, T(X) is a minimal sufficient statistic for .

21. EXAMPLE
X~N(,2) where 2 is known. For a r.s. of size n, find minimal ss for .
Note: A minimal ss is also not unique. Any 1-to-1 function is also a minimal ss.

22. ANCILLARY STATISTIC
A statistic S(X) whose distribution does not depend on the parameter  is called an ancillary statistic.
Unlike a ss, an ancillary statistic contains no information about .

23. Example Example 6.1.8 in Casella & Berger, page 257:
Let Xi~Unif(θ,θ+1) for i=1,2,…,n
Then, range R=X(n)-X(1) is an ancillary statistic because its pdf does not depend on θ.

24. COMPLETENESS
Let {f(x; ), } be a family of pdfs (or pmfs) and U(x) be an arbitrary function of x not depending on . If
requires that the function itself equal to 0 for all possible values of x; then we say that this family is a complete family of pdfs (or pmfs).
i.e., the only unbiased estimator of 0 is 0 itself.

25. EXAMPLES
1. Show that the family {Bin(n=2,); 0<<1} is complete.

26. EXAMPLES
2. X~Uniform(,). Show that the family {f(x;), >0} is not complete.

27. COMPLETE AND SUFFICIENT STATISTICS (css)
Y is a complete and sufficient statistic (css) for  if Y is a ss for  and the family
is complete.
The pdf of Y.
1) Y is a ss for .
2) u(Y) is an arbitrary function of Y.
E(u(Y))=0 for all  implies that u(y)=0
for all possible Y=y.

28. BASU THEOREM
If T(X) is a complete and minimal sufficient statistic, then T(X) is independent of every ancillary statistic.
Example: X~N(,2).
(n-1)S2/ 2 ~ S2
Ancillary statistic for 
By Basu theorem, and S2 are independent.

29. BASU THEOREM Example: Let T=X1+ X2 and U=X1 - X2
We know that T is a complete minimal ss.
U~N(0, 22)  distribution free of 
 T and U are independent by Basu’s Theorem
X1, X2~N(,2), independent, 2 known.

30. Problems
Let be a random sample from a Bernoulli distribution with parameter p.
Find the maximum likelihood estimator (MLE) of p. 
Is this an unbiased estimator of p?

31. Problems
If Xi are normally distributed random variables with mean μ and variance σ2, what is an unbiased estimator of σ2?

32. Problems
Suppose that are i.i.d. random variables on the interval [0; 1] with the density function,
where α> 0 is a parameter to be estimated from the sample. Find a sufficient statistic for α.