On a variance stabilizing model and its application to genomic data

Filidor Vilca, Mariana Rodrigues-Motta, Víctor Leiva

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


In this paper, we propose a model based on a class of symmetric distributions, which avoids the transformation of data, stabilizes the variance of the observations, and provides robust estimation of parameters and high flexibility for modeling different types of data. Probabilistic and statistical aspects of this new model are developed throughout the article, which include mathematical properties, estimation of parameters and inference. The obtained results are illustrated by means of real genomic data.

Original languageEnglish
Pages (from-to)2354-2371
Number of pages18
JournalJournal of Applied Statistics
Issue number11
StatePublished - Nov 2013
Externally publishedYes


  • EM algorithm
  • Johnson system distributions
  • maximum-likelihood method
  • non-normality
  • normal scale mixture distributions
  • transformations


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