TY - JOUR
T1 - Random sampling of skewed distributions does not necessarily imply Taylor’s law
JF - Proceedings of the National Academy of Sciences
JO - Proc Natl Acad Sci USA
SP - E3156
LP - E3156
DO - 10.1073/pnas.1507266112
VL - 112
IS - 25
AU - Chen, Youhua
Y1 - 2015/06/23
UR - http://www.pnas.org/content/112/25/E3156.abstract
N2 - Cohen and Xu (1) claim that random samples of any skewed distributions with four finite moments would give rise to Taylor’s law (TL). In fact, skewed distributions do not necessarily generate data following TL. Some highly skewed distributions can generate random data rejecting the law. Here, I show examples for this using beta, lognormal, and Poisson distributions (the last one is used for comparison).I followed the same random sampling and calculation procedure (1): Applied to each of the 10,000 copies of 100 × 100 random matrices generated from a specific skewed distribution, the ordinary least-squares regression is used to calculate the parameters of TL and test the evidence of the law by checking the associated P values of coefficients b and c in the models log … ↵1Email: haydi{at}126.com.
ER -