Loading in 5 sec....

Mean Squared Error and Maximum LikelihoodPowerPoint Presentation

Mean Squared Error and Maximum Likelihood

- 122 Views
- Uploaded on

Download Presentation
## PowerPoint Slideshow about ' Mean Squared Error and Maximum Likelihood' - makana

**An Image/Link below is provided (as is) to download presentation**

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript

### Mean Squared Error and Maximum Likelihood

Lecture XVIII

Mean Squared Error

- As stated in our discussion on closeness, one potential measure for the goodness of an estimator is

- In the preceding example, the mean square error of the estimate can be written as:
where q is the true parameter value between zero and one.

- This expected value is conditioned on the probability of estimate can be written as:T at each level value of q.

MSEs of Each Estimator estimate can be written as:

- Definition 7.2.1. Let estimate can be written as:X and Y be two estimators of q. We say that X is better (or more efficient) than Y if E(X-q)2E(Y-q) for all q in Q and strictly less than for at least one q in Q.

- When an estimator is dominated by another estimator, the dominated estimator is inadmissable.
- Definition 7.2.2. Let q be an estimator of q. We say that q is inadmissible if there is another estimator which is better in the sense that it produces a lower mean square error of the estimate. An estimator that is not inadmissible is admissible.

Strategies for Choosing an Estimator: dominated estimator is inadmissable.

- Subjective strategy: This strategy considers the likely outcome of q and selects the estimator that is best in that likely neighborhood.
- Minimax Strategy: According to the minimax strategy, we choose the estimator for which the largest possible value of the mean squared error is the smallest:

- Definition 7.2.3: Let dominated estimator is inadmissable.q^ be an estimator of q. It is a minimax estimator if for any other estimator of q~ , we have:

Best Linear Unbiased Estimator: dominated estimator is inadmissable.

- Definition 7.2.4: q^ is said to be an unbiased estimator of q if
for all q in Q. We call

bias

- In our previous discussion dominated estimator is inadmissable.T and S are unbiased estimators while W is biased.
- Theorem 7.2.10: The mean squared error is the sum of the variance and the bias squared. That is, for any estimator q^ of q

- Theorem 7.2.11 Let { dominated estimator is inadmissable.Xi} i=1,2,…n be independent and have a common mean m and variance s2. Consider the class of linear estimators of m which can be written in the form
and impose the unbaisedness condition

Then dominated estimator is inadmissable.

for all ai satisfying the unbiasedness condition. Further, this condition holds with equality only for ai=1/n.

- To prove these points note that the dominated estimator is inadmissable.ais must sum to one for unbiasedness
- The final condition can be demonstrated through the identity

- Theorem 7.2.12: Consider the problem of minimizing dominated estimator is inadmissable.
with respect to {ai} subject to the condition

The solution to this problem is given by dominated estimator is inadmissable.

Asymptotic Properties dominated estimator is inadmissable.

- Definition 7.2.5. We say that q^ is a consistent estimator of q if

Maximum Likelihood dominated estimator is inadmissable.

- The basic concept behind maximum likelihood estimation is to choose that set of parameters that maximize the likelihood of drawing a particular sample.
- Let the sample be X={5,6,7,8,10}. The probability of each of these points based on the unknown mean, m, can be written as

- Assuming that the sample is independent so that the joint distribution function can be written as the product of the marginal distribution functions, the probability of drawing the entire sample based on a given mean can then be written as:

- The value of distribution function can be written as the product of the marginal distribution functions, the probability of drawing the entire sample based on a given mean can then be written as:m that maximize the likelihood function of the sample can then be defined by
Under the current scenario, we find it easier, however, to maximize the natural logarithm of the likelihood function:

Download Presentation

Connecting to Server..