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1 Chapter 3 Kolmogorov-Smirnov Tests There are many situations where experimenters need to know what is the distribution of the population of their interest. For example, if they want to use a parametric test it is often assumed that the population under investigation is normal. In this chapter we consider Kolmogorov-Smirnov tests for verifying that a sample comes from a population with some known distribution and also that two populations have the same distribution. 3.1 The one-sample test Let x 1,...,x m be observations on continuous i.i.d. r.vs X 1,...,X m with a c.d.f. F. We want to test the hypothesis where F 0 is a known c.d.f. H 0 : F(x) = F 0 (x) for all x, (3.1) The Kolmogorov-Smirnov test statistic D n is defined by D n = sup ˆF(x) F 0 (x), (3.2) x R where ˆF is an empirical cumulative distribution defined as ˆF(x) = #(i : x i x). (3.3) n Note that supremum (3.2) must occur at one of the observed values x i or to the left of x i. The null distribution of the statistic D n can be obtained by simulation or, for large samples, using the Kolmogorov-Smirnov s distribution function. 27

2 28 CHAPTER 3. KOLMOGOROV-SMIRNOV TESTS u 0.6 U x_u X Figure 3.1: Continuous Cumulative Distribution Function Simulation of the null distribution We may approximate the null distribution of D n by simulation. For this we use the standard uniform random variable. Lemma 3.1 Let X be a continuous r.v. with a c.d.f. F and let U = F(X). Then U Uniform[0, 1]. Proof Let u [0, 1]. X is continuous so xu RF(x u ) = u. See Figure (3.1). Now, F(u) = P(U < u) = P(F(X) < F(x u )) = P(X < x u ) = F(x u ) = u So, F(u) = u and r.v. U is uniform on [0, 1]. To perform the simulation of D n do the following generate random samples of size n from the standard uniform distribution U[0, 1] with the c.d.f. F(u) = u, find maximum absolute difference between F(u) and the empirical distribution ˆF(u) for the generated sample repeat this N times to get the approximate distribution of D n

3 3.1. THE ONE-SAMPLE TEST 29 Small Example Five independent weighings of a standard weight (in gm 10 6 ) give the following discrepancies from the supposed true weight: 1.2, 0.2, 0.6, 0.8, 1.0. Are the discrepancies sampled from N(0, 1)? We set the null hypothesis as H 0 : F(x) = F 0 (x) where F 0 (x) = Φ(x), i.e., it is the c.d.f. of a standard normal r.v. X. To calculate the value of the test function (3.2) we need the empirical c.d.f. for the data and also the values of Φ at the data points. The empirical c.d.f. Calculations: ˆF(x) = 0 for x < for 1.2 x < for 1.0 x < for 0.6 x < for 0.2 x < for x 0.8 x ˆF(x) Φ(x) ˆF(x) Φ(x) Hence, the observed value of D n, say d 0, is d 0 = What is the null distribution of D n? We have n = 5. Suppose we have randomly generated the following five values from the standard uniform distribution:

4 30 CHAPTER 3. KOLMOGOROV-SMIRNOV TESTS F(x) d_max x Figure 3.2: Calculating d max D 5 gets value d max = See Figure 3.2 Another random sample of 5 uniform r.vs will give another value d max. Repeating this procedure we simulate a set of values for D 5. Then, having the approximate null distribution of the test statistic we may calculate the p value of the observed d 0. Below is a simulated distribution of D 5 using a GenStat program **** ********************* ******************************************** ********************************************** ********************************** ************************ **************** ******* *** * This shows that d 0 = is well in the middle of the distribution and so the data do not contradict the null hypothesis that the discrepancies are normally distributed with zero mean and variance equal to one.

5 3.1. THE ONE-SAMPLE TEST Kolmogorov-Smirnov s approximation of the null distribution The approximation is given by the following theorem. Theorem 3.1 Let F 0 be a continuous c.d.f., and let X 1,...,X n be a sequence of i.i.d. r.vs with the c.d.f. F 0. Then 1. The null distribution of D n does not depend on F 0 ; it depends only on n. 2. If n the distribution of nd n is asymptotically Kolmogorov s distribution with the c.d.f. Q(x) = 1 2 ( 1) k 1 e 2k2 x 2, (3.4) k=1 that is lim n P( nd n x) = Q(x). Example: Fire Occurrences A natural reserve in Australia had 15 fires from the beginning of this year. The fires occurred on the following days of the year: 4, 18, 32, 37, 56, 64, 78, 89, 104, 134, 154, 178, 190, 220, 256. A researcher claims that the time between the occurrences of fire in the reserve, say X, follows an exponential distribution, i.e., X Exp(λ), where λ = Is the claim justified? Calculations will be shown during the lectures.

6 32 CHAPTER 3. KOLMOGOROV-SMIRNOV TESTS 3.2 The two-sample test Let x 1,...,x m be observations on i.i.d. r.vs X 1,...,X m with a c.d.f. F 1, y 1,...,y n be observations on i.i.d. r.vs Y 1,...,Y n with a c.d.f. F 2. We are interested in testing the null hypothesis of the form H 0 : F 1 (x) = F 2 (x) for all x against the alternative H 0 : F 1 (x) F 2 (x) Theorem 3.2 Let X 1,...,X m and Y 1,...,Y n be i.i.d. r.vs with a common continuous c.d.f. and let ˆF1 and ˆF 2 be empirical c.d.fs of X s and Y s, respectively. Furthermore, let Then we have where Q(t) is given by (3.4) D m,n = sup ˆF 1 (t) ˆF 2 (t). (3.5) t ( ) mn lim P m,n m + n t = Q(t), (3.6) Hence, the function D m,n may serve as a test statistic for our null hypothesis. It has asymptotic Kolmogorov-Smirnov s distribution. NOTES The Kolmogorov-Smirnov (K-S) two-sample test is an alternative to the MWW test. The MWW test is more powerful when H 1 is the location shift. The K-S test has reasonable power against a range of alternative hypotheses. For small samples we may simulate the null distribution of D m,n applying standard uniform distribution U[0, 1]. Learning the mechanics example will be given during the lectures.

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