professor of Psychology Rand R.Wilcox, University of Southern California Written by Marina Haldna
<ul><li>To introduce and compare different statistical methods </li></ul>
<ul><li>Introduction </li></ul><ul><li>Methods </li></ul><ul><li>Results </li></ul><ul><li>Conclusion </li></ul>
<ul><li>Typical situatsions : </li></ul><ul><li>before-after </li></ul><ul><li>two places  with  parallel  measurements </...
<ul><li>Data are missing at random. </li></ul><ul><li>The reason why a data point is missing is not related to its actual ...
<ul><li>Simple strategy is to compute a confidence interval using a normal or t-distribution, this approach may be unsatis...
<ul><li>Median </li></ul><ul><li>20% trimmed mean </li></ul><ul><li>Difference between the marginal trimmed means </li></ul>
<ul><li>M1(means) </li></ul><ul><li>M2(trimmed means) </li></ul><ul><li>M3 –bootstrap method with trimmed means  </li></ul...
<ul><li>To check the properties of the methods ,   one sample of  correlated  and an other non-correlated  data from bivar...
<ul><li>In terms of Type I errors </li></ul><ul><li>The method for comparing means can be unsatisfactory  </li></ul><ul><l...
<ul><li>Statistics means never having to say you  a re certain </li></ul><ul><li>Statistics is the art of never having to ...
 
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Haldna presentation

  1. 1. professor of Psychology Rand R.Wilcox, University of Southern California Written by Marina Haldna
  2. 2. <ul><li>To introduce and compare different statistical methods </li></ul>
  3. 3. <ul><li>Introduction </li></ul><ul><li>Methods </li></ul><ul><li>Results </li></ul><ul><li>Conclusion </li></ul>
  4. 4. <ul><li>Typical situatsions : </li></ul><ul><li>before-after </li></ul><ul><li>two places with parallel measurements </li></ul><ul><li>Every pair of sampled data has a important information to make a correct conclusion </li></ul>
  5. 5. <ul><li>Data are missing at random. </li></ul><ul><li>The reason why a data point is missing is not related to its actual value </li></ul>
  6. 6. <ul><li>Simple strategy is to compute a confidence interval using a normal or t-distribution, this approach may be unsatisfactory (Liang et al. 2008) </li></ul><ul><li>Even if there are non-missing data, low power arise when sampling from a heavy-tiled distribution (Wilcox, 2005) </li></ul>
  7. 7. <ul><li>Median </li></ul><ul><li>20% trimmed mean </li></ul><ul><li>Difference between the marginal trimmed means </li></ul>
  8. 8. <ul><li>M1(means) </li></ul><ul><li>M2(trimmed means) </li></ul><ul><li>M3 –bootstrap method with trimmed means </li></ul><ul><li>M4-medians </li></ul><ul><li>In terms of efficiency, the median generally performs better than 20% trimmed mean (Wilcox,2006) </li></ul>
  9. 9. <ul><li>To check the properties of the methods , one sample of correlated and an other non-correlated data from bivariate distribution were used </li></ul><ul><li>Simulations were repeated 3000 times, sample size was taken 30. </li></ul><ul><li>Two different cases for missing dates: </li></ul><ul><li>5 from one and 5 from an other group </li></ul><ul><li>10 from one and 0 from an other group </li></ul>
  10. 10. <ul><li>In terms of Type I errors </li></ul><ul><li>The method for comparing means can be unsatisfactory </li></ul><ul><li>Percentile bootstrap method with 20% trimmed means performed well </li></ul><ul><li>In terms of power the M2 and M3 are more satisfactory </li></ul>
  11. 11. <ul><li>Statistics means never having to say you a re certain </li></ul><ul><li>Statistics is the art of never having to say you a re wrong </li></ul><ul><li>( http://www.btinternet.com/~se16/hgb/statjoke.htm ) </li></ul>
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