Comparisons of outlier tests for potency bioassays

2019 ◽  
Vol 19 (3) ◽  
pp. 230-242 ◽  
Author(s):  
Perceval Sondag ◽  
Lingmin Zeng ◽  
Binbing Yu ◽  
Harry Yang ◽  
Steven Novick
Keyword(s):  
Outlier Tests

Controlling the size of multivariate outlier tests with the MCD estimator of scatter

2008 ◽  
Vol 19 (3) ◽  
pp. 341-353 ◽  
Author(s):  
Andrea Cerioli ◽  
Marco Riani ◽  
Anthony C. Atkinson
Keyword(s):  
Outlier Tests

O-Blue for Outlier Tests

2016 ◽  
Vol 5 (3) ◽  
pp. 469-479
Author(s):  
Arvind Pandey ◽  
Nibha Srivastava
Keyword(s):  
Outlier Tests

Outlier Tests and Chemical Assays

1993 ◽  
Vol 10 (4) ◽  
pp. 221-232 ◽  
Author(s):  
Sanford Bolton
Keyword(s):  
Outlier Tests

scholarly journals Evaluation of the minimum sampling design for population genomic and microsatellite studies. An analysis based on wild maize

2020 ◽  
Author(s):  
Jonás A. Aguirre-Liguori ◽  
Javier A. Luna-Sánchez ◽  
Jaime Gasca-Pineda ◽  
Luis E. Eguiarte
Keyword(s):  
Evolutionary History ◽  
Isolation By Distance ◽  
Molecular Ecology ◽  
Population Genomic ◽  
History Of ◽  
Outlier Tests ◽  
Landscape Genetic ◽  
Genomic Regions ◽  
Computational Resources ◽  
Number Of Individuals

ABSTRACTMassive parallel sequencing is revolutionizing the field of molecular ecology by allowing to understand better the evolutionary history of populations and species, and to detect genomic regions that could be under selection. However, the needed economic and computational resources generate a tradeoff between the amount of loci that can be obtained and the number of populations or individuals that can be sequenced. In this work, we analyzed and compared two extensive genomic and one large microsatellite datasets consisting of empirical data. We generated different subsampling designs by changing the number of loci, individuals, populations and individuals per population to test for deviations in classic population genetics parameters (HS, FIS, FST) and landscape genetic tests (isolation by distance and environment, central abundance hypothesis). We also tested the effect of sampling different number of populations in the detection of outlier SNPs. We found that the microsatellite dataset is very sensitive to the number of individuals sampled when obtaining summary statistics. FIS was particularly sensitive to a low sampling of individuals in the genomic and microsatellite datasets. For the genomic datasets, we found that as long as many populations are sampled, few individuals and loci are needed. For all datasets we found that increasing the number of population sampled is important to obtain precise landscape genetic estimates. Finally, we corroborated that outlier tests are sensitive to the number of populations sampled. We conclude by proposing different sampling designs depending on the objectives.

Influence diagnostics and outlier tests for semiparametric mixed models

2002 ◽  
Vol 64 (3) ◽  
pp. 565-579 ◽  
Author(s):  
Wing-Kam Fung ◽  
Zhong-Yi Zhu ◽  
Bo-Cheng Wei ◽  
Xuming He
Keyword(s):  
Mixed Models ◽  
Influence Diagnostics ◽  
Outlier Tests
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