Abstract

CHAVES, Emilio José. LAPLACE CRITERION: FUNDAMENTAL PREMISE IN STATISTICAL INDUCTION. Tend. [online]. 2015, vol.16, n.1, pp.51-64. ISSN 0124-8693.  https://doi.org/10.22267/rtend.151601.32.

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 distri-bution 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.

Keywords : Statistical induction fundamentals; Fitting models; Numerical methods; Lorenz Curves and CDF; Random samples.