Journal ArticleDOI
Background and threshold: critical comparison of methods of determination
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TLDR
There is no good reason to continue to use the [mean+/-2 sdev] rule, originally proposed as a 'filter' to identify approximately 2(1/2)% of the data at each extreme for further inspection at a time when computers to do the drudgery of numerical operations were not widely available and no other practical methods existed.About:
This article is published in Science of The Total Environment.The article was published on 2005-06-15. It has received 686 citations till now. The article focuses on the topics: Median absolute deviation & Exploratory data analysis.read more
Citations
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Geochemical background--concept and reality.
TL;DR: Based on data from two subcontinental-scale geochemical mapping projects, it is shown that trying to define 'a background' for a large area is fraught with problems and that background may change from area to area within a region and between regions.
Journal ArticleDOI
High-throughput quantitative polymerase chain reaction in picoliter droplets.
Margaret Macris Kiss,Lori Ortoleva-Donnelly,N. Reginald Beer,Jason Warner,Christopher G. Bailey,Bill W. Colston,Jonathon M. Rothberg,Darren R. Link,John H. Leamon +8 more
TL;DR: It is shown that amplification of a 245-bp adenovirus product can be detected and quantified in 35 min at starting template concentrations as low as 1 template molecule/167 droplets (0.003 pg/microL), demonstrating both the accuracy and sensitivity of this platform for limiting dilution and digital PCR applications.
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Multivariate outlier detection in exploration geochemistry
TL;DR: It is demonstrated that important processes such as the input of metals from contamination sources and the contribution of sea-salts via marine aerosols to the soil can be identified and separated.
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The interpretation of geochemical survey data
TL;DR: Mining data includes the application of multivariate data analysis and statistical techniques, combined with geographical information systems, and can significantly assist the task of data interpretation and subsequent model building.
References
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Book
Robust Regression and Outlier Detection
TL;DR: This paper presents the results of a two-year study of the statistical treatment of outliers in the context of one-Dimensional Location and its applications to discrete-time reinforcement learning.
Book
Outliers in Statistical Data
Vic Barnett,Toby Lewis +1 more
TL;DR: In this article, the authors present an updated version of the reference work on outliers, including new areas of study such as outliers in direction data as well as developments in fields such as discordancy tests for univariate and multivariate samples.
Book
Robust statistics: the approach based on influence functions
TL;DR: This paper presents a meta-modelling framework for estimating the values of Covariance Matrices and Multivariate Location using one-Dimensional and Multidimensional Estimators.