Efficient resampling methods for nonsmooth estimating functions.
We propose a simple and general resampling strategy to estimate variances for parameter estimators derived from nonsmooth estimating functions. This approach applies to a wide variety of semiparametric and nonparametric problems in biostatistics. It does not require solving estimating equations and is thus much faster than the existing resampling procedures. Its usefulness is illustrated with heteroscedastic quantile regression and censored data rank regression. Numerical results based on simulated and real data are provided.
Zeng
Donglin
D
Department of Biostatistics, CB 7420, University of North Carolina, Chapel Hill, NC 27599-7420, USA.
Lin
D Y
DY
D050197:Atherosclerosis / Q000453:epidemiology / Q000209:etiology; D015982:Bias; D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D006801:Humans; D008875:Middle Aged; D009101:Multiple Myeloma / Q000503:physiopathology; D011237:Predictive Value of Tests; D011897:Random Allocation; D012044:Regression Analysis; D015203:Reproducibility of Results; D018401:Sample Size; D014894:Weights and Measures