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Since this rather amazing fact was discovered in 1881 by the American astronomer
Newcomb (1881), many scientist have been searching about members of the outlaws
number family. Newcomb noticed that the pages of the logarithm books containing
numbers starting with 1 were much more worn than the other pages. After analyzing
several sets of naturally occurring data Newcomb went on to derive what later became
Benford’s law. As a tribute to the figure of Newcomb we call this phenomenon, the
Newcomb  Benford’s Law.
We start by establishing a connection between the Microarray and Stock Index
data sets. That can be seen as an extension of the work done by Hoyle David C.
(2002) and Ley (1996). Most of the analysis have been made using Classical and
Bayesian statistics. Here is explained differences between the different scopes on the
hypothesis testing between models Berger J.O. and Pericchi L. R. (2001). Finally,
the applications of this concepts to the different types of data including Microarray,
Stock Index and Electoral Process.
There are several results on constrained data, the most relevant is the Constrained
Newcomb Benford Law and most of the Bayesian Analysis covered, applied to this
problem.
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