Input Data Analysis 3 Goodness-of-Fit Tests

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2. Input Data Analysis. Activity I: Hypothesizing families of distributions (?What does it look like?")Activity II: Estimation of parameters (?How is it represented?")Activity III: Determining how representative the fitted distributions are (?How accurate is the representation?"). . 3. Goodness-of

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Input Data Analysis 3 Goodness-of-Fit Tests

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1. 1 Input Data Analysis (3) Goodness-of-Fit Tests

2. 2 Input Data Analysis Activity I: Hypothesizing families of distributions (What does it look like?) Activity II: Estimation of parameters (How is it represented?) Activity III: Determining how representative the fitted distributions are (How accurate is the representation?)

3. 3 Goodness-of-Fit Test Used to test hypothesis that a given number of data points (e.g., collected from the system - field data) are independent samples from a particular probability distribution Observation: X1, X2, X3, , Xn H0: Xis are IID random variables with distribution function F e.g., uniform, normal, exponential, etc.

4. 4 Goodness-of-Fit Test Note: Failure to reject H0 should NOT be interpreted as accepting H0 as being true Because these tests are not very powerful for small to moderate sample size n (often real field data are limited in size) - not sensitive to subtle deviation between sample data and the fitted distribution If n is very large, then these test will almost always reject H0, since H0 is virtually never exactly true But for practical reasons, nearly correct is acceptable

5. 5 The Chi-Square Test Pearson (1900) First, divide the entire range of the fitted distribution into k adjacent intervals with equal lengths except the 1st and the last: [a0, a1), [a1, a2), [a2, a3), , [ak-1, ak] a0 = -?, ak = +? ? First: (-?, a1), Last: [ak, +?) Then tally Nj = number of Xis in the jth interval [aj-1, aj) for j = 1, 2, , k (note: ?(j=1 to k) Nj = n) Compute the expected Proportion Pj of the Xis that would fall in the jth interval

6. 6 The Chi-Square Test For continuous case For discrete data (our testing interest)

7. 7 The Chi-Square Test Test statistics Since npj is the expected number of n Xis that would fall in the jth interval If H0 were true, we would expect ?2 to be small Reject H0, if ?2 is too large Testing for large n with an approximate ? Reject H0 is: ?2 > ?2k-1,1-? (L&K: Fig. 6.45) where ?2k-1,1-? is the upper 1-? critical point for a Chi-Square distribution with k-1 d.f.

9. 9 The Chi-Square Test The Chi-Square test is only valid, i.e., of level ?, asymptotically as n ? ? (L&K: Fig. 6.46) Also, need to estimate m parameters of the fitted distribution (m ? 1) When maximum likelihood estimates are used, if H0 is true, as n ? ? the distribution of ?2 converges to a distribution that lies between the distribution functions of Chi-Square distributions with k-1 and k-m-1 d.f.

11. 11 The Chi-Square Test Clear-Cut Cases If ?2 > ?2k-1,1-? ? reject H0 If ?2 < ?2k-1,1-? ? do not reject H0 What if ?2k-m-1,1-? ? ?2 ? ?2k-1,1-? Recommend: Reject H0 only if ?2 > ?2k-1,1-? ? conservative i.e., the actual probability ? of committing a type I error (reject H0 when H0 is true) is at least as small as the stated probability ? Result: loss of power (small probability of rejecting a false H0) of the test

12. 12 The Chi-Square Test Clear-Cut Cases (Continue) Normally m?2 and if k is fairly large, the difference between ?2k-m-1,1-? and ?2k-1,1-? is very small Difficulty: Choose of number of intervals 1. Equal length 2. Equal-probable (p1= p2 = = pk) - difficult in practice Less conservative: (maybe more practical) If ?2 > ?2k-m-1,1-? ? reject H0

13. 13 The Chi-Square Test Ex: Time between Arrival (TBA). 60 data points (all greater than 0) and Exp(20.1) Cell Frequency Theoretical prop. npj -? [0,10) 19 23.52 0.869 [10,20) 16 14.31 0.200 [20,30) 12 8.69 1.261 [30,40) 8 5.29 1.388 [40,50) +? 5 3.21 1.236 ? = 60 ?2 = 4.954 f(x) = (1/20.1) e-x/20.1 for x > 0 F(x) = 1 - e-x/20.1 [F(x) = ?(from 0 to x) f(x) dx] df = 5 - 1 - 1 = 3 ? ?2 = 4.954 < ?23,1-0.05 = 7.81

14. 14 The Chi-Square Test Ex: Inventory Model n = 156 observations on the (discrete) number of items demanded in a week from an inventory over a 3-year period. The weekly demands are: 0 (59) 1 (26) 2 (24) 3 (18) 4 (12) 5 (5) 6 (4) 7 (3) 9 (3) 11 (2)

15. 15 The Chi-Square Test Ex: Inventory Model (Continue) Summary Statistics Min. = 0.000 Max. = 11.000 Mode = 0 Mean = 1.891 Median = 1.000 Variance = 5.285 Lexis ratio ? = ?2/? = 2.795 Skewness ? = E[(X - ?)3]/(?2)3/2 = 1.655 Try to fit a geometric distribution geom(0.346) With roughly equal number of observations: 3 intervals j Interval Nj npj 1 {0} 59 53.960 0.471 2 {1,2} 50 58.382 1.203 3 {3,4,} 47 43.658 0.256 ?2 = 1.930 Compare with ?2 = 1.930 < ?23-1, 0.90 = 4.605 H0 is not rejected at ? = 0.10 level

16. Kolmogorov-Smirnov (K-S) Test The Kolmogorov-Smirnov test (KS test) is a form of minimum distance estimation used as a non-parametric test of equality of one-dimensional probability distributions used to compare a sample with a reference probability distribution (one-sample KS test), or to compare two samples (two-sample KS test). The KolmogorovSmirnov statistic quantifies a distance between the empirical distribution of the sample and the cumulative distribution function of the reference distribution, or between the empirical distribution functions of two samples. The null distribution of this statistic is calculated under the null hypothesis that the samples are drawn from the same distribution (in the two-sample case) or that the sample is drawn from the reference distribution (in the one-sample case). In each case, the distributions considered under the null hypothesis are continuous distributions but are otherwise unrestricted. 16

17. 17 Kolmogorov-Smirnov (K-S) Test Compare an empirical distribution function with the distribution function of the hypothesized distribution Advantages K-S tests do not require to group data in any way, so no information is lost; this also eliminates the troublesome problem of interval specification K-S tests are valid (exactly) for any sample size n (in the all-parameters-known case), whereas Chi-Square tests are valid only in an asymptotic sense K-S tests tend to be more powerful than Chi-Square tests against many alternative distributions

18. 18 Kolmogorov-Smirnov (K-S) Test Disadvantages The range of applicability is more limited than that for Chi-Square tests For discrete data, the required critical values are not readily available and must be computed using a complicated set of formulas The original form of the K-S test is valid only if all the parameters of the hypothesized distribution are known and the distribution is continuous Allow for estimation of the parameters in the cases of normal (lognormal), exponential, and Weibull distributions

19. 19 Kolmogorov-Smirnov (K-S) Test An empirical distribution Fn(x) from data X1,, Xn: or where is the indicator function, equal to 1 if Xi=x and equal to 0 otherwise. Thus, Fn(x) is a step function If is the fitted distribution function, a natural assessment of goodness of fit is some measure between Fn(x) and

20. Kolmogorov-Smirnov (K-S) Test 20

21. 21 Kolmogorov-Smirnov (K-S) Test Dn can be calculated by: Notes: Direct computation of Dn+ and Dn requires sorting the data to obtain X(i)s For moderate values of n (up to several hundreds), sorting can be done quickly by simple methods If n is large, sorting becomes expensive! A large value of Dn indicates a poor fit, so that it is to reject the null hypothesis H0 if Dn exceeds some constant dn,1-?, where ? is specified level of the test

24. 24 Kolmogorov-Smirnov (K-S) Test Case 1: If all parameters of are known None of the parameters is estimated in any way from the data, the distribution of Dn does not depend on , assuming that it is continuous Instead of testing for Dn > dn,1-?, we reject H0 if where c1-? are given in the all-parameters-known row of Table 6.14 This case is the original form of the K-S test

26. 26 Kolmogorov-Smirnov (K-S) Test Case 2: Suppose that the hypothesized distribution is N(?, ?2) with both ? and ?2 unknown Estimate ? and ?2 by X(n) and S2(n), respectively Define the distribution function to be N(X(n), S2(n)) Using this , Dn is computed in the same way We reject H0 if where c1-? are in the N(X(n), S2(n)) row of Table 6.14 This case includes a K-S test for the lognormal distribution if the Xis are the logarithms of the basic data points

27. 27 Kolmogorov-Smirnov (K-S) Test Case 3: Suppose the hypothesized distribution is expo(?) with ? unknown ? is estimated by its MLE X(n) Define to be the expo(X(n)) distribution function Using this , Dn is computed We reject H0 if where c1-? are given in the Expo(X(n)) row of Table 6.14

28. 28 Kolmogorov-Smirnov (K-S) Test Case 4: Suppose the hypothesized distribution is Weibull with both shape parameter ? and scale parameter ? unknown Estimate parameters ? and ? by their respective MLEs is taken to be Weibull (MLEs of ? and ? ) Dn is computed in the usual fashion We reject H0 if the adjusted K-S statistic is greater than the modified critical value c*1-? given in Table 6.15 Note that critical values are available only for certain sample sizes n, and that the critical values for n = 50 and ? are, fortunately, very similar

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