haldna presentation
TRANSCRIPT
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professor of Psychology Rand R.Wilcox, University of Southern California
Written by Marina Haldna
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To introduce and compare different statistical methods
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Introduction Methods Results Conclusion
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Typical situatsions : before-after two places with parallel measurements
Every pair of sampled data has a important information to make a correct conclusion
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Data are missing at random. The reason why a data point is missing is
not related to its actual value
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Simple strategy is to compute a confidence interval using a normal or t-distribution, this approach may be unsatisfactory (Liang et al. 2008)
Even if there are non-missing data, low power arise when sampling from a heavy-tiled distribution (Wilcox, 2005)
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Median 20% trimmed mean Difference between the marginal trimmed
means
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M1(means) M2(trimmed means) M3 –bootstrap method with trimmed means M4-medians
In terms of efficiency, the median generally performs better than 20% trimmed mean (Wilcox,2006)
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To check the properties of the methods, one sample of correlated and an other non-correlated data from bivariate distribution were used
Simulations were repeated 3000 times, sample size was taken 30.
Two different cases for missing dates: 5 from one and 5 from an other group10 from one and 0 from an other group
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In terms of Type I errors The method for comparing means can be
unsatisfactory Percentile bootstrap method with 20%
trimmed means performed well
In terms of power the M2 and M3 are more satisfactory
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Statistics means never having to say you are certain
Statistics is the art of never having to say you are wrong
(http://www.btinternet.com/~se16/hgb/statjoke.htm)
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