>Can someone help me about detection of outliers using jackknife after >bootstrap algorithm?
A simple procedure is to calculate the mean of the bootstrap statistics for all bootstrap samples that omit the first of the original observations. Repeat for the second, third, ... original observation. You now have $n$ means, and can look at these for outliers. A similar approach is to calculate means of bootstrap statistics for samples that <include> (rather than omit) each of the the original observations. Both of those approaches can suffer from considerable random variability. Provided the number of bootstrap samples is large, a better approach is to use linear regression, where y = the vector of bootstrap statistics, length R X = R by n matrix, with X[i, j] = the number of times original observation j is included in bootstrap sample i and without an intercept. The $n$ regression coefficients give estimates of the influence of the original observations, and you can look for outliers in these influence estimates. For comparison, the first simple procedure above corresponds to taking averages of y for rows with X[, j] = 0, and the "similar approach" to averaging y for rows with X[, j] > 0. For further discussion see Hesterberg, Tim C. (1995), "Tail-Specific Linear Approximations for Efficient Bootstrap Simulations", Journal of Computational and Graphical Statistics, 4(2), 113-133. Hesterberg, Tim C. and Stephen J. Ellis (1999), "Linear Approximations for Functional Statistics in Large-Sample Applications", Technical Report No. 86, Research Department, MathSoft, Inc., 1700 Westlake Ave. N., Suite 500, Seattle, WA 98109. http://home.comcast.net/~timhesterberg/articles/tech86-linear.pdf ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.