1. Chapter 6 parameter estimation

2. § 6.1 Point estimation
Set the distribution function of population X is F(x), which contains the unknown parameters ,we call any function of a sample point estimator.

3. 1, Moment estimate
If population distribution contains k unknown parameters,method of moments estimator are found by equating the first k sample moments to the corresponding k population moments,and solving the resulting system of simultaneous equations.

4. Example 6.1
suppose are iid ,We have according to the method of moments,we yield the moments estimator of is

5. Example 6.2
Suppose are iid , We have ,hence we must solve the equation
Solving for yields the moments estimator of is

6. Example 6.3 suppose are iid,and the common pdf is
， is a known positive constant，we have
Let ,we yield the moment estimator of is

7. 2, Maximum likelihood estimate
Consider a random sample from a distribution having pdf or probability distribution , are unknown parameters,The joint pdf of is ,this may be regard as a function of ,when so regarded,it is called the likelihood function L of the random sample,and we write

8. Suppose that we can find a nontrivial function of ,say ,when is replaced by ,the likelihood function L is a maximum,then the statistic will be called a maximum likelihood estimator of ,and will be denoted by the symbol

9. The function can be maximized by setting the first partial derivation of ,with respect to ,equal to zero,that is to say
and solving the resulting equation for ,which is the maximum likelihood estimator of

10. Example 6.4
Let denote a random sample from a distribution that is ,we shall find , the maximum likelihood estimator of .

11. Example 6.5
Let denote a random sample from a distribution that is are unknown parameters, we shall find , the maximum likelihood estimators of

12. Example 6.6 If the probability distribution of is
( ) is unknown parameter,we observed the following eight values of ,
3, 1, 3, 0, 3, 1, 2, 3,
Find the moment estimate and the maximum likelihood estimate of .

13. Example 6.7
Let denote a random sample from a distribution that is , Find the maximum likelihood estimators of

14. 3, Methods of evaluating estimators
3.1 Unbiased estimator
If an estimator satisfies that ,the estimator is called unbiased.

15. Example 6.8
Let denote a random sample from , ,Prove that and are the unbiased estimator of respectly.

16. 3.2 Efficient estimator Let and are both the unbiased estimator of ,if
We say is more efficient than If the number of sample is fixed,and the variance of is less than or equal to the variance of every other unbiased estimator of ,we say is the efficient estimator of

17. Example 6.9 Consider a random sample from a distribution having pdf
,Prove that and are both the unbiased estimators of ,and when , is more efficient than

18. 3.3 Uniform estimator If for any ,we have ,
We call is the uniform estimator of

19. §6.2 Interval estimation
An interval estimator of a real-valued parameter is any pair of function and of a sample that satisfy
for all sample x.if the sample x is observed,the inference
is made.The random interval is called an interval estimator;If ,we call the confidence coefficient of is we often set =0.01,0.05,0.1 etc.

20. 1, Interval estimator for mean in normal population
Let denote a random sample from a distribution that is is known, is unknown parameter,
Set
, We have By the definition of we have , ,
, the interval estimator of with the confidence coefficient is

21. Determine a 95 per cent confidence interval for .
Example 6.10
Let a random sample of size 10 from the normal distribution
They are
18.3, 17.5, 18.1, 17.7, 17.9, 18.5, 18, 18.1,17.8,17.9,
Determine a 95 per cent confidence interval for .

22. 1.2 Let denote a random sample from a distribution that is and are unknown parameters, Set
We have ,so
Hence the interval estimator of with the confidence coefficient is .

23. Example 6.11
Let a random sample of size 9 from the normal distribution ,They are
0.497,0.506，0.518，0.524，0.488，0.510，0.510，0.515，0.512，
Determine a 99 per cent confidence interval for

24. 2, Interval estimator for variance in normal population
Let denote a random sample from a distribution that is and are unknown parameters,
Set , We have ,so ,
, So the interval estimator of with the confidence coefficient is .

25. Example 6.12
Let a random sample of size 9 from the normal distribution ,They are
600, 612, 598, 583, 609, 607, 592, 588, 593,
Determine a 95 per cent confidence interval for

26. 3, Interval estimator for difference of means and ratio of variance in two normal populations
Let denote a random sample of size from a distribution that is denote a random sample of size from a distribution that is where , are unknown parameters, the two random samples are independent, What is the interval estimator of and with the confidence coefficient ?

28. 3.1 Difference of means If ,but is unknown,set , So ,hence
, So the interval estimator of with the confidence coefficient is .

29. 3.2 ratio of variance Set , so .hence
So the interval estimator of with the confidence coefficient is

30. Example 6.13
Let a random sample of size 4 from the normal distribution ,They are Another random sample of size 5 from the normal distribution ,They are The two random samples are independent,determine the interval estimator of and with the confidence coefficient 0.95.