Standard errors and covariance matrices for smoothed rank estimators

2005/03/01 by B. M. Brown, You-Gan Wang, You‐Gan Wang · 6 citations
Mathematics · Physics and Astronomy · #Advanced Statistical Methods and Models #Statistical and numerical algorithms #Statistical Mechanics and Entropy

paper · doi: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.

Cited by

• Smoothing combined estimating equations in quantile regression for longitudinal data
• Weighted quantile regression for longitudinal data
• A Provable Smoothing Approach for High Dimensional Generalized Regression with Applications in Genomics
• Is it even rainier in North Vancouver? A non-parametric rank-based test\n for semicontinuous longitudinal data
• Generalized linear mixed quantile regression with panel data. [europepmc]
• Smoothed quantile residual life regression analysis with application to the Korea HIV/AIDS cohort study. [europepmc]

Related

• Consistency of Ranking Estimators
• Statistical properties of the estimator using covariance matrix
• Estimation of Low-Rank Covariance Function
• Rank penalized estimators for high-dimensional matrices
• Sparse and Low-Rank Covariance Matrices Estimation