Laplace criterion: fundamental premise in statistical induction

Authors

DOI:

https://doi.org/10.22267/rtend.151601.32

Keywords:

Statistical induction fundamentals, Fitting models, Numerical methods, Lorenz Curves and CDF, Random samples

Abstract

It discusses the rule or Laplace Criterion and fundaments its use to build the Lorenz Curve, LC, from datasets. It presents samples and graphs of inferred fitting models of LC and CDF; it comments the limits of the model. Method separates real media U, from adimentional CDF to work it as CDF(real)=U(real)*CDF(in medias). The purpose is to give fundamentals to univariate statistical inference of positive datases using Laplace Criterion, standard mathematics and Boolean sets theory. This nonparametric method assumes identical 1/N frequencies for N data without using a-priori distribution functions. Given its simplicity, it is proposed to apply it in statistical education and research as a theoretical element, prior to the handling of multivariate analysis.

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Author Biography

Emilio José Chaves, Universidad de los Andes

Ingeniero Mecánico, Universidad de los Andes. Investigador independiente. Correos electrónicos:
chavesej@hotmail.com, ejotach@gmail.com

References

CAMPOS, Alberto (2004). Laplace: Ensayo filosófico sobre las probabilidades. Revista Colombiana de Estadística. Vol. 27, No. 2, p. 64.

LAPLACE, Pierre Simon de (1902). A Philosophical Essay on probabilities. John Wiley and Sons. Translated from 6th. (French edition). Ps 11-12 and 61. Consulted in Sept/04/1914 at https://archive.org/stream/philosophicaless00lapluoft#page/n5/mode/2up

Published

2015-04-30

How to Cite

Chaves, E. J. (2015). Laplace criterion: fundamental premise in statistical induction. Tendencias, 16(1), 51–64. https://doi.org/10.22267/rtend.151601.32

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