Estimates of the Regression Coefficient Based on Kendall's Tau

Pranab Kumar Sen · Journal of the American Statistical Association · 1968

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Pranab Kumar Sen

Publisher

Journal of the American Statistical Association

Year

1968

Language

English

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Abstract The least squares estimator of a regression coefficient β 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 β based 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 ≠ ti , 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.

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APA 7

Sen, P. K. (1968). Estimates of the Regression Coefficient Based on Kendall's Tau. https://doi.org/10.1080/01621459.1968.10480934

MLA

Sen, Pranab Kumar. "Estimates of the Regression Coefficient Based on Kendall's Tau." 1968. https://doi.org/10.1080/01621459.1968.10480934.

Chicago

Sen, Pranab Kumar. 1968. "Estimates of the Regression Coefficient Based on Kendall's Tau.". https://doi.org/10.1080/01621459.1968.10480934.

Harvard

Sen, P. K. 1968, Estimates of the Regression Coefficient Based on Kendall's Tau, Journal of the American Statistical Association, available at: https://doi.org/10.1080/01621459.1968.10480934 [Accessed 7 Aug. 2026].

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Title: Estimates of the Regression Coefficient Based on Kendall's Tau

Author / contributors: Pranab Kumar Sen

Publisher: Journal of the American Statistical Association

Publication year: 1968

Language: English

Subjects

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Mathematics; Estimator; Statistics; Confidence interval; Nonparametric statistics; Asymptotic distribution; Simple linear regression; Rank (graph theory); Linear regression; Combinatorics