Estimates of the regression coefficient based on Kendall's tau. Sen, P. K. J Am Stat Assoc, 63:1379–1389, 1968.
Estimates of the regression coefficient based on Kendall's tau [link]Paper  abstract   bibtex   
The least squares estimator of a regression coefficient βis vulnerable to gross errors and the associated confidence interval is, in addition, sensitive to non-normality of the parent distribution. In this paper, a simple and robust (point as well as interval) estimator of βbased on Kendall's [6] rank correlation tau is studied. The point estimator is the median of the set of slopes (Yj - Yi)/(tj - ti) joining pairs of points with ti \ne tj, and is unbiased. The confidence interval is also determined by two order statistics of this set of slopes. Various properties of these estimators are studied and compared with those of the least squares and some other nonparametric estimators.
@Article{sen68estimates,
  author    = {Sen, Pranab Kumar},
  title     = {Estimates of the regression coefficient based on {Kendall's} tau},
  journal   = {J Am Stat Assoc},
  year      = {1968},
  volume    = {63},
  pages     = {1379--1389},
  abstract  = {The least squares estimator of a regression coefficient \beta is vulnerable to gross errors and the associated confidence interval is, in addition, sensitive to non-normality of the parent distribution. In this paper, a simple and robust (point as well as interval) estimator of \beta based on Kendall's [6] rank correlation tau is studied. The point estimator is the median of the set of slopes (Y<sub>j</sub> - Y<sub>i</sub>)/(t<sub>j</sub> - t<sub>i</sub>) joining pairs of points with t<sub>i</sub> \ne t<sub>j</sub>, and is unbiased. The confidence interval is also determined by two order statistics of this set of slopes. Various properties of these estimators are studied and compared with those of the least squares and some other nonparametric estimators.},
  owner     = {Sebastian},
  timestamp = {2013.10.10},
  url       = {http://www.jstor.org/stable/2285891},
}

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