1. Confidence Interval & Unbiased Estimator Review and Foreword

2. Central limit theorem vs. the weak law of large numbers

3. Weak law vs. strong law Personal research Search on the web or the library Compare and tell me why

4. Cont.

5. Maximum Likelihood estimator Suppose the i.i.d. random variables X 1, X 2, … X n, whose joint distribution is assumed given except for an unknown parameter θ, are to be observed and constituted a random sample. f(x 1,x 2,…,x n )=f(x 1 )f(x 2 )…f(x n ), The value of likelihood function f(x 1,x 2,…,x n / θ ) will be determined by the observed sample (x 1,x 2,…,x n ) if the true value of θ could also be found. Differentiate on the θ and let the first order condition equal to zero, and then rearrange the random variables X1, X2, … Xn to obtain θ.

6. Confidence interval

7. Confidence vs. Probability Probability is used to describe the distribution of a certain random variable (interval) Confidence (trust) is used to argue how the specific sampling consequence would approach to the reality (population)

8. 100(1-α)% Confidence intervals

9. 100(1-α)% confidence intervals for (μ 1 - μ 2 )

10. Approximate 100(1-α)% confidence intervals for p

11. Unbiased estimators

12. Linear combination of several unbiased estimators If d 1,d 2,d 3,d 4 … d n are independent unbiased estimators If a new estimator with the form, d=λ 1 d 1 +λ 2 d 2 +λ 3 d 3 + … λ n d n and λ 1 +λ 2 + … λ n =1, it will also be an unbiased estimator. The mean square error of any estimator is equal to its variance plus the square of the bias r(d, θ)=E[(d(X)-θ)2]=E[d-E(d)2]+(E[d]-θ)2

13. The Bayes estimator

14. The value of additional information The Bayes estimator The set of observed sample revised the prior θ distribution Smaller variance of posterior θ distribution Ref. pp.274-275