Please use this identifier to cite or link to this item:
|Title:||Statistical inference for induced L-statistics: A random perturbation approach||Authors:||Xu, J.
|Issue Date:||Oct-2009||Citation:||Xu, J., Zhao, L., Leng, C. (2009-10). Statistical inference for induced L-statistics: A random perturbation approach. Journal of Nonparametric Statistics 21 (7) : 863-876. ScholarBank@NUS Repository. https://doi.org/10.1080/10485250902980584||Abstract:||Suppose that X and Y are two numerical characteristics defined for each individual in a population. In a random sample of (X,Y) with sample size n, denote the rth ordered X variate by Xr:n and the associated Y variate, the induced rth order statistics, by Y[r:n], respectively. Induced order statistics arise naturally in the context of selection where individuals ought to be selected by their ranks in a related X value due to difficulty or h∑gh costs of obtaining Y at the time of selection. The induced L-statistics, which take the form of, are very useful in regression analysis, especially when the observations are subject to a type-II censoring scheme with respect to the dependent variable, or when the regression function at a given quantile of the predictor variable is of interest. The limiting variance of the induced L-statistics involve the underlying regression function and inferences based on nonparametric estimation are often unstable. In this paper, we consider the distributional approximation of the induced L-statistics by the random perturbation method. Large sample properties of the randomly perturbed induced L-statistics are established. Numerical studies are also conducted to illustrate the method and to assess its finite-sample performance. © 2009 Taylor & Francis.||Source Title:||Journal of Nonparametric Statistics||URI:||http://scholarbank.nus.edu.sg/handle/10635/105390||ISSN:||10485252||DOI:||10.1080/10485250902980584|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Jan 26, 2023
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.