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New Dispersion Function in the Rank Regression

  • Published : 2002.04.01

Abstract

In this paper we introduce a new score generating (unction for the rank regression in the linear regression model. The score function compares the $\gamma$'th and s\`th power of the tail probabilities of the underlying probability distribution. We show that the rank estimate asymptotically converges to a multivariate normal. further we derive the asymptotic Pitman relative efficiencies and the most efficient values of $\gamma$ and s under the symmetric distribution such as uniform, normal, cauchy and double exponential distributions and the asymmetric distribution such as exponential and lognormal distributions respectively.

Keywords

References

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Cited by

  1. Hypothesis Testing for New Scores in a Linear Model vol.10, pp.3, 2003, https://doi.org/10.5351/CKSS.2003.10.3.1007