A new test is proposed for the problem of comparing two independent groups in terms of some measure of location. The proposed test () uses a one-step M-estimator and a bootstrap-t method with the procedure proposed by Özdemir and Kurt (2006). Eight methods were compared in terms of actual Type I error and power when the underlying distributions differ in skewness and kurtosis under heterogeneity of variances. For the 21 theoretical distributions, the Yuen test with the bootstrap-t method was the most favourable, followed by test. For the five real data sets, the proposed test and percentile bootstrap method with the one-step M-estimator performed best.
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http://dx.doi.org/10.1111/j.2044-8317.2012.02053.x | DOI Listing |
Sensors (Basel)
January 2014
Department of Information and Communication Engineering, Yeungnam University, 214-1 Dae-dong, Gyeongsan-si, Kyongsan 712-749, Gyeongsangbuk-do, Korea.
Trust establishment is an important tool to improve cooperation and enhance security in wireless sensor networks. The core of trust establishment is trust estimation. If a trust estimation method is not robust against attack and misbehavior, the trust values produced will be meaningless, and system performance will be degraded.
View Article and Find Full Text PDFBr J Math Stat Psychol
May 2013
Dokuz Eylül Üniversitesi, Fen Fakültesi İstatistik Bölümü Tınaztepe Yerleşkesi, Buca/İzmir, Turkey.
A new test is proposed for the problem of comparing two independent groups in terms of some measure of location. The proposed test () uses a one-step M-estimator and a bootstrap-t method with the procedure proposed by Özdemir and Kurt (2006). Eight methods were compared in terms of actual Type I error and power when the underlying distributions differ in skewness and kurtosis under heterogeneity of variances.
View Article and Find Full Text PDFBr J Math Stat Psychol
May 2003
Department of Psychology, University of Southern California, Los Angeles, CA 90089, USA.
Wilcox, Keselman, Muska and Cribbie (2000) found a method for comparing the trimmed means of dependent groups that performed well in simulations, in terms of Type I errors, with a sample size as small as 21. Theory and simulations indicate that little power is lost under normality when using trimmed means rather than untrimmed means, and trimmed means can result in substantially higher power when sampling from a heavy-tailed distribution. However, trimmed means suffer from two practical concerns described in this paper.
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