# 2DI36 Statistics. 2DI36 Part II (Chapter 7 of MR)

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1 2DI36 Statistics 2DI36 Part II (Chapter 7 of MR)

2 What Have we Done so Far? Last time we introduced the concept of a dataset and seen how we can represent it in various ways But, how did this dataset came into being? Example: 100 balls from the brand HIT-ME are hit by a machine, and the distance reached by each ball is measured (in meters) ; ; ; ; ; ; ; ; ; 255 ; ; ; ; ; ; ; ; 273 ; ; ; ; ; ; ; ; 267 ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; 252 ; 264 ; ; ; ; ; ; ; ; ; 260 ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; ; 274 ; ; ; ; ; ; ; ; ; ; ; ; ; 261 ; 236 ; ; ; ; ; ; ; ; ; ; ; ; Our goal is to make meaningful statements about all the HIT-ME balls, but using only this sample

3 The Golf Ball Example Sample Population (all HIT-ME golf balls) (a small number of golf balls randomly taken from the population) What can we assume about the sample? Balls were selected randomly from the pool of ALL HIT-ME balls. All balls are produced by the same process and have similar properties The population of all balls is characterized by some unknown parameters these are our main object of interest.

4 A Simple Probabilistic Model Definition: Random Sample This forms the basis of our statistical model. We will assume that our dataset is a realization of these random variables, namely: Important Notational Remark: We will use CAPITAL letters to denote random variables, and the corresponding lower-case letters to denote observed values!!!

5 Tony Soprano s Casino Tony Soprano s casino: There is bingo game in this Mafia casino were you win if a red ball comes out of the cage You are told there is an equal number of red and blue balls in the cage, but you d like to be sure (it seems you are loosing way to often). In particular, the outcome of the game in the last 30 plays was: L, L, L, W, L, L, W, L, W, L, W, L, W, W, W, W, L, W, L, L, L, L, L, L, W, W, L, L, L, W Question: What is a good statistical model in this case?

6 Tony Soprano s Casino The last point is extremely important it means the outcome of each round has no effect on the other rounds. In this model we don t know the value of p and we would like to say something about it

7 The Golf Ball Example This is surprisingly very similar to the previous example, if you think about it in the right way: Imagine there is a HUGE bag with ALL the HIT-ME golf balls in the world (a huge number of them!) Now reach into to bag and take n balls chosen at random. Again, the last point is extremely important, it means that the distance of a golf ball has no effect on the distance of all the other balls.

8 A Simple Probabilistic Model There are many situations that fit, in a way or the other, this exact model: Survey results (you ask a random selection of individuals) Speed of a network link (measurements are noisy) Temperature in a museum room (noisy measurements) Many others (see book ) Definition: Random Sample

9 Statistic and Estimators Now that we have a model, what can we do with it? Definition: Statistic and Sampling Distribution Some statistics can be quite useful: Definition: Estimator and Estimate

10 Example Sample Mean Let s try to characterize the sampling distribution of this estimator:

11 Example Sample Mean Let s try to characterize the sampling distribution of this estimator:

12 Example Sample Mean

13 Example Sample Mean It is nice to see that the value we expect to get with this estimator is always around the true mean µ. Furthermore, the variance of the estimator becomes smaller and smaller as we have more samples!!! This is still not a full characterization of the sample distribution. For that we need to know the exact distribution of X 1,,X n. You learned this 2DI26!!! (Chapter 5 of Mont.)

14 Sample Mean Definition: Sample Mean Proposition:

15 The Central Limit Theorem It turns out that the sampling distribution of the sample mean is approximately normal even if the distribution of the data is not normal!!! This is sort of the fundamental theorem of statistics: Theorem: The Central Limit Theorem (CLT) This is very powerful, as we ll see later on in the course

16 The Central Limit Theorem - Examples Normal Q-Q Plot Sample Quantiles Density Histogram of r.mean 8-4 r.mean 3 4 r.mean Normal Q-Q Plot Sample Quantiles 0.6 Density Theoretical Quantiles Histogram of r.mean Theoretical Quantiles 4

17 The Central Limit Theorem - Remarks The central limit theorem applies almost any time we compute averages, and aggregate data. We will make extensive use of this result, although sometimes we will state consequences of the CLT without proof. Keep in mind that the CLT is an asymptotic result, which means that, for finite n, the result only holds approximately. So, if you have only a small amount of data it might not be adequate Next: How do we construct good estimators? First we must state what we mean by a good estimator

18 Criteria for Evaluating Estimators We would like to ensure the estimator is somewhat close to the true unknown parameter, therefore we need to devise a way to measure the distance between the two Since the estimator is a function of the data, it is a random quantity. Therefore we must take this into account To formalize these notions we need state a number of properties that (might) be desirable for estimators to have, just as unbiasedness, small mean squared error, and low variance.

19 Measures of Error! Definition: Mean Squared Error Clearly, if this is zero then the estimator is perfect. There are of course other ways of measuring the error of an estimator, e.g. We will focus mainly on MSE, because it makes our life easy, but it is worth pointing out that sometimes this is not the most adequate error metric!!!

20 Bias and Variance! Definition: Bias and Variance and Standard Error Definition: Unbiased Estimator For unbiased estimators, the value of the estimate is centered around the true unknown parameter

21 The Importance/Irrelevance of the Bias! Are biased estimators all that bad??? Bottom line: unbiasedness is a desirable, but not essential property a good estimator should have. It turns out the mean squared error can be easily written in terms of bias a variance:

22 Bias/Variance Decomposition! Theorem: Bias-Variance Decomposition not random! = 0

23 Examples Sample Mean! Therefore this estimator is unbiased!!!

24 Examples Sample Mean! So the variance gets smaller and smaller as we have more data

25 Examples Variance Estimator!

26 Examples Variance Estimator!

27 Examples Variance Estimator! This estimator is biased. However, the bias gets smaller as sample size increases However, we can easily remove the bias simply multiplying the estimator by n/(n-1), giving rise to our familiar Definition: Sample Variance

28 Methods of Point Estimation! So far we have described useful properties estimators should have, but haven t given any methodology to construct the estimators There are several ways to do this, and in this course we ll describe only two possibilities: Method of Moments Maximum Likelihood

29 Method of Moments A very simple, but effective idea: equate the sample moments (e.g. sample mean and variance) to the population moments (e.g., true mean and variance)! This yields a series of equations relating the unknown parameters to the data. Definition: k th sample and population moments

30 Method of Moments The right-hand-side is a function of the unknown parameter(s) so we can just equate this function with the left-hand-side, and solve that system of equations for. If the parameter space is d dimensional we will typically need at least d equations. Definition: Method of Moments Estimator

31 Examples

32 Examples

33 Examples Home exercise: Derive the results in the previous slide and the above formulas

34 Maximum Likelihood Estimation This is one of the most common ways of constructing estimators, and perhaps the most well understood. Furthermore these estimators are endowed with many desirable qualities. The main assumption is that the data density/p.m.f. has a distribution with Definition: Maximum Likelihood Estimator

35 Interpretation For discrete random variables the interpretation of the Maximum Likelihood principle is quite easy: The likelihood function is the probability that we observe the realization of the data, if the distribution of the random sample was drawn from. So the maximum likelihood estimator is choosing the parameter that maximizes the probability/likelihood of the observed data.

36 Example

37 Example Home exercise: check that the method of moments yields the same estimator in this case.

38 Example Soprano s Casino

39 Example

40 General Approach to Compute MLEs Definition: likelihood,log-likelihood and score

41 General Approach to Compute MLEs Provided the likelihood is positive and differentiable the general approach to compute MLEs is: Compute the likelihood function Compute the log-likelihood and score Identify the local extremes of the likelihood by equating the score to zero Identify the extreme corresponding to the global maximum Attention: Sometimes you cannot compute the log-likelihood or the score (e.g. see the example of the uniform distribution) Sometimes identifying the zeros of the score is intractable. In that case one must resort to numerical methods (e.g. gradient ascent).

42 Examples!

43 Properties of the MLE! Proposition: Invariance Property Theorem: (informally stated):

44 Examples!

45 Examples!

46 Examples! Unlike we had in the normal data case correcting for the bias is in this case very advantageous!!! So it all depends on the balance between bias and variance

47 Example Beyond the Random Sample!

48 Examples!

49 Examples!

50 Examples!

51 Examples!

52 Examples!

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