A non-parametric coverage interval

作者: Shuo-Huei Lin , Wenyaw Chan , Lin-An Chen

DOI: 10.1088/0026-1394/45/1/N01

关键词:

摘要: Classically the non-parametric coverage interval is estimated by empirical quantiles. We introduce an alternative way for estimating symmetric quantiles given Chen and Chiang (1996 J. Nonparametric Stat. 7 171–85). further show that this has a better precision in sense its asymptotic variances are smaller than classical one.

参考文章(8)
Lin-An Chen, Yuang-Chin Chiang, Symmetric quantile and symmetric trimmed mean for linear regression model Journal of Nonparametric Statistics. ,vol. 7, pp. 171- 185 ,(1996) , 10.1080/10485259608832697
Lin-An Chen, Jing-Ye Huang, Hung-Chia Chen, Parametric coverage interval Metrologia. ,vol. 44, ,(2007) , 10.1088/0026-1394/44/2/N01
David Ruppert, Raymond J. Carroll, Trimmed Least Squares Estimation in the Linear Model Journal of the American Statistical Association. ,vol. 75, pp. 828- 838 ,(1980) , 10.1080/01621459.1980.10477560
Elizabeth A. Wagar, Rhona Souers, Richard C. Friedberg, Paul N. Valenstein, Ana K. Stankovic, The origin of reference intervals. Archives of Pathology & Laboratory Medicine. ,vol. 131, pp. 348- 357 ,(2007) , 10.5858/2007-131-348-TOORI
Allen H Reed, Richard J Henry, William B Mason, Influence of Statistical Method Used on the Resulting Estimate of Normal Range Clinical Chemistry. ,vol. 17, pp. 275- 284 ,(1971) , 10.1093/CLINCHEM/17.4.275
J. M. Singer, P. K. Sen, H. MacGillivray, Large sample methods in statistics ,(1993)
Seong-Ju Kim, The metrically trimmed mean as a robust estimator of location Annals of Statistics. ,vol. 20, pp. 1534- 1547 ,(1992) , 10.1214/AOS/1176348783
Pranab Kumar Sen, Julio M. Singer, Large Sample Methods in Statistics Springer US. ,(1993) , 10.1007/978-1-4899-4491-7