Journal of University of Chinese Academy of Sciences >
A three-stepwise robust statistical method for outlying rainfall observation
Received date: 2008-11-06
Revised date: 2009-09-03
Online published: 2010-01-15
A three-stepwise robust statistical method combining the robust statistical theory with distribution features of rainfall for detection of outliers in telemetry system is described. The proposed robust statistical method adopts the Tukey fence insensitive to outliers as identification bounds and presents a three-stepwise pattern to adapt the distribution of rainfall data. Moreover, the modified method based on dividing precipitation data into several groups further improves detection efficiency. The results show that the new method is suitable to the hydrological need.
ZHAO Chao , HONG Hua-Sheng , ZHU Mu-Lan . A three-stepwise robust statistical method for outlying rainfall observation[J]. Journal of University of Chinese Academy of Sciences, 2010 , 27(1) : 17 -26 . DOI: 10.7523/j.issn.2095-6134.2010.1.003
[1] Barnett V, Lewis T. Outliers in statistical data
[M].UK: John Wiley, 1994.
[2] Han J, Kamber M. Data mining: concepts and techniques
[M]. Morgan Kaufmann Publishers, 2001.
[3] Grubbs F E. Procedures for detecting outlying observations in samples
[J]. Technometrics, 1969,11(1): 1-10.
[4] Grubbs F E, Beck G. Extension of sample sizes and percentage points for significance tests of outlying observations
[J]. Technometrics, 1972, 4(14): 847-853.
[5] Singh D P. Flood frequency modeling and outliers organic geochemistry
[M]. New York: ASCE, 1980.
[6] Hu S Y. Problems with outlier test methods in flood frequency analysis
[J]. Journal of Hydrology, 1987, 96(1- 4): 375-383.
[7] Spencer C S, McCuen R H. Detection of outliers in pearson type Ⅲ data
[J]. Journal of Hydrologic Engineering, 1996,1(1): 2-10.
[8] Bounessah M, Atkin B P. An application of exploratory data analysis (EDA) as a robust non-parametric technique for geochemical mapping in a semi-arid climate
[J]. Applied Geochemistry, 2003, 18: 1185-1195.
[9] Zhou Q, Li S N, Li X P, et al. Detection of outliers and establishment of targets in external quality assessment programs
[J]. Clinica Chimica Acta, 2006, 372:94-97.
[10] Zhou Q, Shen Z Y, Li S N, et al. Robust and traditional statistical methods in the establishment of immunoglobulin E target values in external quality essessment program
[J]. Clinica Chimica Acta, 2008, 387: 66-70.
[11] Daszykowski M, Kaczmarek K, Heyden Y V, et al. Robust statistics in data analysis-A review basic concepts
[J]. Chemometrics and Intelligent Laboratory Systems, 2007, 85: 203-219.
[12] Grubbs F E. Procedures for detecting outlying observations in samples
[J]. Technometrics, 1969, 11(1):1-10.
[13] Narasimhan S, Mah R. Generalized likelihood ratio method for gross error identification
[J]. AIChE J, 1987, 33:1514-1521.
[14] Bao W M, Qu S M, Li Q S, et al. Study of estimation methods of rainfall gauge errors in remote system
[J]. Journal of Hydraulic Engineering, 2003, 4:30-33 (in Chinese). 包为民, 瞿思敏, 李清生, 等. 遥测系统降雨观测误差估计方法研究
[J]. 水利学报, 2003, 4:30-33.
/
| 〈 |
|
〉 |