# Adjusting the Asymmetric Tw

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Adjusting the Asymmetric Tw

Adjusting the Asymmetric Two Sample t-Test Sandy D. Balkin Ernst & Young LLP 1225 Connecticut Avenue, NW Washington, DC 20037 Abstract The Telecommunications Act of 1996 requires that Incumbent Local Exchange Carriers (ILECs) must provide, for a fair price, interconnection services to the customers of a Competitive Local Exchange Carrier (CLEC). To monitor the ILEC's performance, we need formal statistical tests of compliance. Inspection of data on several performance measures reveals severe positive skewness, violating the assumptions of the standard t-test. We also want to detect not only shifts in mean but also increases in variance. Permutation testing would be preferable, but is unwieldy. In response to this need, we present a skewness adjustment to the asymmetric two-sample ttest. We compare the resulting tests with permutation tests. 1 Introduction The Telecommunications Act of 1996 mandates that Incumbent Local Exchange Carriers (ILECs) must provide, if requested, for a fair price, interconnection services to the customers of a Competitive Local Exchange Carrier (CLEC), these service being . . .at least equal in quality to [those] provided by the local exchange carrier to itself. . . As a result, the ILECs are prohibited from oering longdistance services in their home territories until they prove that their networks are open to competitors. The ILECs, sensing a large market potential, are eager to offer long distance as part of a bundle of communications options that include wireless, local calling and Internet services. Thus, there exists an inherent business problem in that providing services to customers of a competitor imposes a clear conict of interest on the ILEC. 2 Statistical Setup To ensure that the CLECs receive the same quality of service the ILEC provides its own customers, we need to Colin L. Mallows AT&T Labs { Research 180 Park Avenue Florham Park, New Jersey 07932 establish formal statistical procedures to monitor its performance. The performance measure data collected are made up of the ILEC's service performance for its own customers (X 's) and for the CLEC's customers (Y 's). The alternatives to the null compliance hypothesis is either E (Y ) > E (X ) or V (Y ) > V (X ): Given two samples X1 ; : : : ; Xm and Y1 ; : : : ; Yn ; we can consider the usual null hypothesis H0 : FX = FY : A preferred choice for evaluating this kind of hypothesis is a permutation test. However, due to some large sample sizes, the number of tests to be performed, and tight reporting deadlines, permutation tests are computationally impractical. If the populations are approximately normal, we can consider using the t test which is calculated from easy to compute summary statistics. Inspection of data on several performance measures reveals severe positive skewness, violating the assumptions of the standard t-test. Also, we want to detect not only shifts in mean but also increases in variance, as either way, the CLEC will be receiving worse service. 3 Asymmetric t-Statistic The t-statistic is of the form X t= Y S where for the usual statistic tpooled S2 = S2 pooled and 2 Spooled = (m If normality holds, tpooled standard alternative 1 m + 1 n 2 + (n 1)S 2 1)SX Y : m+n 2 has optimal power against the HA : E (Y ) > E (X ); V (Y ) = V (X ): However, this test does not have optimal power against alternatives in which V (Y ) may be larger than V (X ). In response to this, Brownie, et. al (1990) present an asymmetric version of the t statistic which uses 4 Adjusting the Asymmetric t- Johnson (1978) derived a skewness adjustment for the one-sample t test. Because of the details of the problem at hand, our goal is to derive an adjustment for the twosample test based on the tasymmetric : The modication of the t statistic obtained in this study uses the CornishFisher expansion for a variable X , CF (X ) = + + (3 = 2 )( 2 2 1 0 Permutation Test Z−Score −2 −1 0 1 2 Asymmetric t−Test Z−Score Figure 1: Scatterplot of Permutation Z-Scores versus Asymmetric T-test Z-Scores. Tests for Parity Service −1 Statistic −1 This adjusted statistic sacrices some degrees of freedom, but if m is not very small, it has better power than tpooled for the alternatives of dierent variances. Brownie, et. al (1990) propose the tasymmetric test for use in a randomized experiment and show that the modied test is more powerful for alternatives where the variance has increased. Their paper also compares these two tests with the Welch test and shows that this can have much smaller power than either the standard tpooled or the modied tasymmetric , especially in the case of most interest to us, namely n << m: Both the ILECs and CLECs would prefer to use a permutation test of the hypothesis, but realize that the computational burden is excessive. Both would welcome an approximation, such as the t statistic, as long as it performs similarly to the permutation test, but can be quickly calculated from summary statistics. Figure 1 is a quantile-quantile plot of the permutation z-scores versus tasymmetric statistics converted to z-scores for samples of a specic performance measure with both group sample sizes greater than six. For this performance measure, the ILEC sample sizes have a mean of 152 and a maximum of 1,488 compared with a mean sample size of 13 and maximum of 57 for the CLECs. We would expect to see the points fall close to the 45-degree line. However, we see that there appears to be some quadratic structure in the plot. Thus, for the Asymmetric t test to be considered as a viable alternative to permutation testing, it must be adjusted for this curvature. 3 : −2 n Tests for Parity Service 3 + 2 m 1 1 1 0 X Permutation Test Z−Score asymmetric = S2 1) + ; where is a standard normal random variable, is the mean of X , and 2 ; 3 ; : : : are the second, third,...central moments of X respectively. −2 S2 −1 0 1 2 Adjusted Asymmetric t−Test Z−Score Figure 2: Scatterplot of Permutation Z-Scores versus Adjusted T-test Z-Scores. The derivation of the adjusted, asymmetric t statistic proceeds as follows. Let the modied t statistic take the form tadj = t + + t2 Assuming all moments of a population exist, the Cornish-Fisher expansion of the numerator of the tasymmetric statistic is Y ) = CF (X "r 1=m2 1=n2 2 + + 1 ( m n 6 1=m + 1=n 1 1 # 1) and for the denominator X where 2 = (4 4 )= 4 . The covariance of X Y 2 is 3 =m so the correlation between and is and SX q p n m+n 1 = 2 . Plugging in the expansion terms and choosing and to cancel the terms of O n 1=2 , we get = tasymmetric + g 6 m + 2n p mn(m + n) t2asymmetric + n m m + 2n where g is an estimate of the standardized third moment. The resulting expression has reduced bias, its distribution is more symmetric, and the contribution of loworder terms due to the correlation between numerator and denominator is eliminated. Since monotone and invertible transformation is desirable, we need to bound the adjustment to ensure the correction is in the appropriate direction. The minimum value of the adjusted t statistic, called tmin is given by solving the equation @tadj =0 @t for t giving tmin = p 3 mn(m + n)=(g (m + 2n)): If tasymmetric tmin we use tasymmetric in the adjustment, else we use tadj = tasymmetric + g 6 Conclusions In order to comply with the the Telecommunications Act of 1996, ILEC rms require a statistical test of parity. The data collected for performance measures often violate the usual assumptions and permutation tests are computationally prohibitive. In response, we developed an asymmetric version of the two-sample t-test which is adjusted for skewness and that is sensitive to alternatives where one of the population variances may have increased. We demonstrate graphically that the adjustments made to the tasymmetric statistic provides results similar to those obtained from permutation tests. Acknowledgements p CF (SX ) = 2 (1 + (2 =m) ) tadj 5 m + 2n n m t2min + m + 2n mn(m + n) p We see in Figure 2 that the quadratic structure of the z-scores has been adjusted for and that the adjusted points are closer to the 45 degree line. The authors wish to thank BellSouth for allowing us to include actual performance measure data in this paper. References Brownie, C., Boos, D., Hughes-Oliver, J., (1990), \Modifying the t and ANOVA F Tests When Treatment is Expected to Increase Variability Relative to Controls," Biometrics, 46, 259{266. Johnson, Norman J., (1978), \Modied t Tests and Condence Intervals for Asymmetrical Populations,", Journal of the American Statistical Association, 73:363, 536{ 544.