Please use this identifier to cite or link to this item: https://doi.org/10.1093/biomet/92.1.149
Title: Standard errors and covariance matrices for smoothed rank estimators
Authors: Brown, B.M.
Wang, Y.-G. 
Keywords: Covariance estimator
Estimating function
Induced smoothing
Kernel estimator
Linearisation
One step estimation
Rank estimation
Sandwich formula
Second-order convergence
Standard error
Wilcoxon estimator
Issue Date: Mar-2005
Citation: Brown, B.M., Wang, Y.-G. (2005-03). Standard errors and covariance matrices for smoothed rank estimators. Biometrika 92 (1) : 149-158. ScholarBank@NUS Repository. https://doi.org/10.1093/biomet/92.1.149
Abstract: A 'pseudo-Bayesian' interpretation of standard errors yields a natural induced smoothing of statistical estimating functions. When applied to rank estimation, the lack of smoothness which prevents standard error estimation is remedied. Efficiency and robustness are preserved, while the smoothed estimation has excellent computational properties. In particular, convergence of the iterative equation for standard error is fast, and standard error calculation becomes asymptotically a one-step procedure. This property also extends to covariance matrix calculation for rank estimates in multi-parameter problems. Examples, and some simple explanations, are given. © 2005 Biometrika Trust.
Source Title: Biometrika
URI: http://scholarbank.nus.edu.sg/handle/10635/105387
ISSN: 00063444
DOI: 10.1093/biomet/92.1.149
Appears in Collections:Staff Publications

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