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References: Higgins (2004) Intro to Modern Nonpar Stat

Nonparametrics.Zip (a compressed version of nonparamtrics) Tom Hettmansperger Department of Statistics, Penn State University. References: Higgins (2004) Intro to Modern Nonpar Stat Hollander and Wolfe (1999) Nonpar Stat Methods Arnold Notes

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References: Higgins (2004) Intro to Modern Nonpar Stat

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  1. Nonparametrics.Zip(a compressed version of nonparamtrics)Tom HettmanspergerDepartment of Statistics, Penn State University References: • Higgins (2004) Intro to Modern Nonpar Stat • Hollander and Wolfe (1999) Nonpar Stat Methods • Arnold Notes • Johnson, Morrell, and Schick (1992) Two-Sample Nonparametric Estimation and Confidence Intervals Under Truncation, Biometrics, 48, 1043-1056. • Website: http://www.stat.wmich.edu/slab/RGLM/

  2. Single Sample Methods

  3. Robust Data Summaries • Graphical Displays • Inference: Confidence Intervals and Hypothesis Tests Location, Spread, Shape CI-Boxplots (notched boxplots) Histograms, dotplots, kernel density estimates.

  4. Absolute MagnitudePlanetary NebulaeMilky Way Abs Mag (n = 81) 17.537 15.845 15.449 12.710 15.499 16.450 14.695 14.878 15.350 12.909 12.873 13.278 15.591 14.550 16.078 15.438 14.741 …

  5. But don’t be too quick to “accept” normality:

  6. Null Hyp: Pop distribution, F(x) is normal The Kolmogorov-Smirnov Statistic The Anderson-Darling Statistic

  7. Anatomy of a 95% CI-Boxplot • Box formed by quartiles and median • IQR (interquartile range) Q3 – Q1 • Whiskers extend from the end of the box to the farthest point within 1.5xIQR. For a normal benchmark distribution, IQR=1.348Stdev and 1.5xIQR=2Stdev. Outliers beyond the whiskers are more than 2.7 stdevs from the median. For a normal distribution this should happen about .7% of the time. Pseudo Stdev = .75xIQR

  8. The confidence interval and hypothesis test

  9. Additional Remarks: The median is a robust measure of location. It is not affected by outliers. It is efficient when the population has heavier tails than a normal population. The sign test is also robust and insensitive to outliers. It is efficient when the tails are heavier than those of a normal population. Similarly for the confidence interval. In addition, the test and the confidence interval are distribution free and do not depend on the shape of the underlying population to determine critical values or confidence coefficients. They are only 64% efficient relative to the mean and t-test when the population is normal. If the population is symmetric then the Wilcoxon Signed Rank statistic can be used, and it is robust against outliers and 95% efficient relative to the t-test.

  10. Two-Sample Methods

  11. Two-Sample Comparisons • 85% CI-Boxplots • Mann-Whitney-Wilcoxon Rank Sum Statistic • Estimate of difference in locations • Test of difference in locations • Confidence Interval for difference in locations • Levene’s Rank Statistic for differences in scale • or variance.

  12. Why 85% Confidence Intervals? We have the following test of Rule: reject the null hyp if the 85% confidence intervals do not overlap. The significance level is close to 5% provided the ratio of sample sizes is less than 3.

  13. Mann-Whitney-Wilcoxon Statistic: The sign statistic on the pairwise differences. Unlike the sign test (64% efficiency for normal population, the MWW test has 95.5% efficiency for a normal population. And it is robust against outliers in either sample.

  14. Mann-Whitney Test and CI: App Mag, Abs Mag N Median App Mag (M-31) 360 14.540 Abs Mag (MW) 81 -10.557 Point estimate for d is 24.900 95.0 Percent CI for d is (24.530,25.256) W = 94140.0 Test of d=0 vs d not equal 0 is significant at 0.0000 What is W?

  15. What about spread or scale differences between the two populations? Below we shift the MW observations to the right by 24.9 to line up with M-31. Variable StDev IQR PseudoStdev MW 1.804 2.420 1.815 M-31 1.195 1.489 1.117

  16. Levene’s Rank Test Compute |Y – Med(Y)| and |X – Med(X)|, called absolute deviations. Apply MWW to the absolute deviations. (Rank the absolute deviations) The test rejects equal spreads in the two populations when difference in average ranks of the absolute deviations is too large. Idea: After we have centered the data, then if the null hypothesis of no difference in spreads is true, all permutations of the combined data are roughly equally likely. (Permutation Principle) So randomly select a large set of the permutations say B permutations. Assign the first n to the Y sample and the remaining m to the X sample and compute MMW on the absolute deviations. The approximate p-value is #MMW > original MMW divided by B.

  17. Difference of rank mean abso devs 51.9793 So we easily reject the null hypothesis of no difference in spreads and conclude that the two populations have significantly different spreads.

  18. One Sample Methods k-Sample Methods Two Sample Methods

  19. Variable Mean StDev Median .75IQR Skew Kurtosis Messier 31 22.685 0.969 23.028 1.069 -0.67 -0.67 Messier 81 24.298 0.274 24.371 0.336 -0.49 -0.68 NGC 3379 26.139 0.267 26.230 0.317 -0.64 -0.48 NGC 4494 26.654 0.225 26.659 0.252 -0.36 -0.55 NGC 4382 26.905 0.201 26.974 0.208 -1.06 1.08 All one-sample and two-sample methods can be applied one at a time or two at a time. Plots, summaries, inferences. We begin k-sample methods by asking if the location differences between the NGC nebulae are statistically significant. We will briefly discuss issues of truncation.

  20. Kruskal-Wallis Test on NGC sub N Median Ave Rank Z 1 45 26.23 29.6 -9.39 2 101 26.66 104.5 0.36 3 59 26.97 156.4 8.19 Overall 205 103.0 KW = 116.70 DF = 2 P = 0.000 This test can be followed by multiple comparisons. For example, if we assign a family error rate of .09, then we would conduct 3 MWW tests, each at a level of .03. (Bonferroni)

  21. What to do about truncation. • See a statistician • Read the Johnson, Morrell, and Schick reference. and then • see a statistician. • Here is the problem: Suppose we want to estimate the difference in locations • between two populations: F(x) and G(y) = F(y – d). • But (with right truncation at a) the observations come from Suppose d > 0 and so we want to shift the X-sample to the right toward the truncation point. As we shift the Xs, some will pass the truncation point and will be eliminated from the data set. This changes the sample sizes and requires adjustment when computing the corresponding MWW to see if it is equal to its expectation. See the reference for details.

  22. What more can we do? • Multiple regression • Analysis of designed experiments (AOV) • Analysis of covariance • Multivariate analysis These analyses can be carried out using the website: http://www.stat.wmich.edu/slab/RGLM/

  23. Professor Lundquist, in a seminar on compulsive thinkers, illustrates his brain stapling technique.

  24. The End

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