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On a variance stabilizing model and its application to genomic data

Author

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  • Filidor Vilca
  • Mariana Rodrigues-Motta
  • V�ctor Leiva

Abstract

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.

Suggested Citation

  • Filidor Vilca & Mariana Rodrigues-Motta & V�ctor Leiva, 2013. "On a variance stabilizing model and its application to genomic data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(11), pages 2354-2371, November.
    • Handle: RePEc:taf:japsta:v:40:y:2013:i:11:p:2354-2371
    DOI: 10.1080/02664763.2013.811480
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    Cited by:

    1. Aykroyd, Robert G. & Leiva, Víctor & Ruggeri, Fabrizio, 2019. "Recent developments of control charts, identification of big data sources and future trends of current research," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 221-232.
    2. Tsai, Arthur C. & Liou, Michelle & Simak, Maria & Cheng, Philip E., 2017. "On hyperbolic transformations to normality," Computational Statistics & Data Analysis, Elsevier, vol. 115(C), pages 250-266.

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