Journal of Nuclear Science, Engineering and Technology (JONSAT)
In cooperation with the Iranian Nuclear Society

The Outlier Sample Effects on Multivariate Statistical Data Processing in Geochemical Stream Sediment Survey (Moghangegh Region, NW of Iran)

Document Type : Research Paper

Authors

Y Ghanbari
A Habibnia
A Memar
Abstract
In geochemical stream sediment surveys in Moghangegh Region in north west of Iran, sheet 1:50,000, 152 samples were collected and after the analyze and processing of data, it revealed that Yb, Sc, Ni, Li, Eu, Cd, Co, as contents in one sample is far higher than other samples. After detecting this sample as an outlier sample, the effect of this sample on multivariate statistical data processing for destructive effects of outlier sample in geochemical exploration was investigated. Pearson and Spearman correlation coefficient methods and cluster analysis were used for multivariate studies and the scatter plot of some elements together the regression profiles are given in case of 152 and 151 samples and the results are compared. After investigation of multivariate statistical data processing results, it was realized that results of existence of outlier samples may appear as the following relations between elements:
- true relation between two elements, which have no outlier frequency in the outlier sample.
- false relation between two elements which one of them has outlier frequency in the outlier sample.
- complete false relation between two elements which both have outlier frequency in the outlier sample.

Highlights

 

  1. 1.    A.R.H Swan, M. Sndilands, P. Mccabe, “Introduction to geological data analysis,” Backwill Science, p. 446 (1995).

 

  1. 2.    ع.ا. حسنی پاک، ”تحلیل داده­های اکتشافی،“ انتشارات دانشگاه  تهران (1380).                                                                             

 

  1. 3.    B.R. Clarke, C.R. Heathcote, “Robust estimation of k-component univariate normal mixtures,” Ann. Inst. Statist. Math. 46, 83–93 (1994).

 

  1. 4.    C. Robert, Szava-Kovats, “Outlier-resistant errors-in-variables regression: anomaly recognition and grain-size correction in stream sediments,” J. Applied Geochemistry. No. 17, pp.1149-1157 (2002).

 

  1. 5.    P.J. Rousseeuw, A. Leroy, “Robust regression and outlier detection,” John Wiley & Sons, Inc, New York (1987).

 

W.B. Size, “Use and abuse of statistical methods in the earth sciences,” New York, Oxford (1986).                                                                   

Keywords

Outlier Sample
Multivariate Statistical Data Processing
Geochemical Stream Sediment Survey

  1.  

    1. 1.    A.R.H Swan, M. Sndilands, P. Mccabe, “Introduction to geological data analysis,” Backwill Science, p. 446 (1995).

     

    1. 2.    ع.ا. حسنی پاک، ”تحلیل داده­های اکتشافی،“ انتشارات دانشگاه  تهران (1380).                                                                             

     

    1. 3.    B.R. Clarke, C.R. Heathcote, “Robust estimation of k-component univariate normal mixtures,” Ann. Inst. Statist. Math. 46, 83–93 (1994).

     

    1. 4.    C. Robert, Szava-Kovats, “Outlier-resistant errors-in-variables regression: anomaly recognition and grain-size correction in stream sediments,” J. Applied Geochemistry. No. 17, pp.1149-1157 (2002).

     

    1. 5.    P.J. Rousseeuw, A. Leroy, “Robust regression and outlier detection,” John Wiley & Sons, Inc, New York (1987).

     

    W.B. Size, “Use and abuse of statistical methods in the earth sciences,” New York, Oxford (1986).                                                                   

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