A simplified procedure of linear regression in a preliminary analysis

Silvia Facchinetti, Umberto Magagnoli

Research output: Contribution to journalArticlepeer-review

Abstract

The analysis of a statistical large data-set can be led by the study of a particularly interesting variable Y – regressed – and an explicative variable X, chosen among the remained variables, conjointly observed. The study gives a simplified procedure to obtain the functional link of the variables y = y (x) by a partition of the data-set into m subsets, in which the observations are synthesized by location indices (mean or median) of X and Y. Polynomial models for y (x) of order r are co nsidered to verify the characteristics of the given procedure, in particular for r = 1 and 2. The distributions of the parameter estimators are obtained by simulation, when the fitting is done for m = r + 1. Comparisons of the results, in terms of distribution and efficiency, are made with the results obtained by the ordinary least square methods. The study also gives some considerations on the consistency of the estimated parameters obtained by the given procedure.
Original languageEnglish
Pages (from-to)153-170
Number of pages18
JournalSTATISTICA
Volume2010
Publication statusPublished - 2010

Keywords

  • linear regression
  • simplified procedure

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