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International Conference on Analytical and Computational Methods in Probability Theory

ACMPT 2017: Analytical and Computational Methods in Probability Theory pp 497–508Cite as

The HRD-Algorithm: A General Method for Parametric Estimation of Two-Component Mixture Models

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10684))

Abstract

We introduce a novel approach to estimate the parameters of a mixture of two distributions. The method combines a grid approach with the method of moments and can be applied to a wide range of two-component mixture models. The grid approach enables the use of parallel computing and the method can easily be combined with resampling techniques. We derive the method for the special cases when the data are described by the mixture of two Weibull distributions or the mixture of two normal distributions, and apply the method on gene expression data from 409 \(ER+\) breast cancer patients.

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Acknowledgments

This work was supported by grants from the Swedish Research Council, Dnr 340-2013-5185 (P. R.), the Kempe Foundations, Dnr JCK-1315 (D. K., P. R.), and the Faculty of Science and Technology, Umeå University (Yu. B., D. K., P. R.).

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Authors and Affiliations

  1. Department of Mathematics and Mathematical Statistics, Umeå University, 901 87, Umeå, Sweden

    Yuri Belyaev, David Källberg & Patrik Rydén

Authors
  1. Yuri Belyaev
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  2. David Källberg
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  3. Patrik Rydén
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Corresponding author

Correspondence to David Källberg .

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Editors and Affiliations

  1. Department of Applied Probability and Informatics, RUDN University, Moscow, Russia

    Vladimir V. Rykov

  2. City University of Hong Kong, Hong Kong, Hong Kong

    Nozer D. Singpurwalla

  3. Steklov Mathematical Institute of Russian Academy of Sciences, Moscow, Russia

    Andrey M. Zubkov

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Belyaev, Y., Källberg, D., Rydén, P. (2017). The HRD-Algorithm: A General Method for Parametric Estimation of Two-Component Mixture Models. In: Rykov, V., Singpurwalla, N., Zubkov, A. (eds) Analytical and Computational Methods in Probability Theory. ACMPT 2017. Lecture Notes in Computer Science(), vol 10684. Springer, Cham. https://doi.org/10.1007/978-3-319-71504-9_41

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  • DOI: https://doi.org/10.1007/978-3-319-71504-9_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71503-2

  • Online ISBN: 978-3-319-71504-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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