Tests Jean-Yves Le Boudec

Contents 1.The Neyman Pearson framework 2.Likelihood Ratio Tests 3.ANOVA 4.Asymptotic Results 5.Other Tests 1

Tests Tests are used to give a binary answer to hypotheses of a statistical nature Ex: is A better than B? Ex: does this data come from a normal distribution ? Ex: does factor n influence the result ? 2

Example: Non Paired Data Is red better than blue ? For data set (a) answer is clear (by inspection of confidence interval) no test required 3

Is this data normal ? 4

5.1 The Neyman-Pearson Framework 5

Example: Non Paired Data; Is Red better than Blue ? 6

Example: Non Paired Data; Is Red better than Blue ? ANOVA Model 7

Critical Region, Size and Power 8

Example : Paired Data Is A better than B ? 9

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Power 11

12 Grey Zone power

p-value of a test 13

p-value of a test 14

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Tests are just tests 16

17 Grey Zone power

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2. Likelihood Ratio Test A special case of Neyman-Pearson A Systematic Method to define tests, of general applicability 19

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Example : Paired Data Is A better than B ? 21

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A Classical Test: Student Test The model : The hypotheses : 23

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Example : Paired Data Is A better than B ? 25

Here it is the same as a Conf. Interval 26

Test versus Confidence Intervals If you can have a confidence interval, use it instead of a test 27

The “Simple Goodness of Fit” Test Model Hypotheses 28

1. compute likelihood ratio statistic 29

2. compute p-value 30

Mendel’s Peas P= 0.92 ± 0.05 => Accept H 0 31

3 ANOVA Often used as “Magic Tool” Important to understand the underlying assumptions Model Data comes from iid normal sample with unknown means and same variance Hypotheses 32

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The ANOVA Theorem We build a likelihood ratio statistic test The assumption that data is normal and variance is the same allows an explicit computation it becomes a least square problem = a geometrical problem we need to compute orthogonal projections on M and M 0 35

The ANOVA Theorem 36

Geometrical Interpretation Accept H 0 if SS2 is small The theorem tells us what “small” means in a statistical sense 37

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ANOVA Output: Network Monitoring 39

The Fisher-F distribution 40

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Compare Test to Confidence Intervals For non paired data, we cannot simply compute the difference However CI is sufficient for parameter set 1 Tests disambiguate parameter sets 2 and 3 42

Test the assumptions of the test… Need to test the assumptions Normal In each group: qqplot… Same variance 43

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4 Asymptotic Results 45 2 x Likelihood ratio statistic

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The chi-square distribution 47

Asymptotic Result Applicable when central limit theorem holds If applicable, radically simple Compute likelihood ratio statistic Inspect and find the order p (nb of dimensions that H1 adds to H0) This is equivalent to 2 optimization subproblems lrs = = max likelihood under H1 - max likelihood under H0 The p-value is 48

Composite Goodness of Fit Test We want to test the hypothesis that an iid sample has a distribution that comes from a given parametric family 49

Apply the Generic Method Compute likelihood ratio statistic Compute p-value Either use MC or the large n asymptotic 50

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Is it normal ? 52

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Mendel’s Peas P= 0.92 ± 0.05 => Accept H 0 55

Test of Independence Model Hypotheses 56

Apply the generic method 57

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5 Other Tests Simple Goodness of Fit Model: iid data Hypotheses: H 0 common distrib has cdf F() H 1 common distrib is anything Kolmogorov-Smirnov: under H 0, the distribution of is independent of F() 59

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Anderson-Darling An alternative to K-S, less sensitive to “outliers” 61

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Jarque Bera test of normality (Chapter 4) Based on Kurtosis and Skewness Should be 0 for normal distribution 64

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Robust Tests Median Test Model : iid sample Hypotheses 66

Median Test 67

Wilcoxon Signed Rank Test 68

Wilcoxon Rank Sum Test Model: X i and Y j independent samples, each is iid Hypotheses: H 0 both have same distribution H 1 the distributions differ by a location shift 69

Wilcoxon Rank Sum Test 70

Turning Point 71 the indices of X (1), X (2), X (3) form a permutation, uniform among 6 values