TY - JOUR
T1 - On a variance stabilizing model and its application to genomic data
AU - Vilca, Filidor
AU - Rodrigues-Motta, Mariana
AU - Leiva, Víctor
N1 - Funding Information:
The authors wish to thank the Editor-in-Chief, Prof. Robert Aykroyd, and three anonymous referees for their constructive comments on an earlier version of this manuscript, which resulted in this improved version. The research work of Víctor Leiva was partially supported by FONDECYT 1120879 grant from Chile and FACEPE from Brazil. The work of Filidor Vilca was partially supported by a research fellowship from Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Brazil.
PY - 2013/11
Y1 - 2013/11
N2 - 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.
AB - 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.
KW - EM algorithm
KW - Johnson system distributions
KW - maximum-likelihood method
KW - non-normality
KW - normal scale mixture distributions
KW - transformations
UR - http://www.scopus.com/inward/record.url?scp=84885376799&partnerID=8YFLogxK
U2 - 10.1080/02664763.2013.811480
DO - 10.1080/02664763.2013.811480
M3 - Article
AN - SCOPUS:84885376799
SN - 0266-4763
VL - 40
SP - 2354
EP - 2371
JO - Journal of Applied Statistics
JF - Journal of Applied Statistics
IS - 11
ER -