Revistes Publicacions URV: SORT - Statistics and Operations Research Transactions> 2015

A comparison of computational approaches for maximum likelihood estimation of the Dirichlet parameters on high-dimensional data

  • Identification data

    Identifier: RP:2427
    Handle: https://hdl.handle.net/20.500.11797/RP2427
    Authors:
    Wehrens, RonGiordan, Marco
    Abstract:
    Likelihood estimates of the Dirichlet distribution parameters can be obtained only through numerical algorithms. Such algorithms can provide estimates outside the correct range for the parameters and/or can require a large amount of iterations to reach convergence. These problems can be aggravated if good starting values are not provided. In this paper we discuss several approaches that can partially avoid these problems providing a good trade-off between efficiency and stability. The performances of these approaches are compared on high-dimensional real and simulated data.

  • Others:

    URV's Author/s: Wehrens, Ron Giordan, Marco
    Keywords: Levenberg-Marquardt algorithm, re-parametrization, starting values, metabolomics data
    Abstract: Likelihood estimates of the Dirichlet distribution parameters can be obtained only through numerical algorithms. Such algorithms can provide estimates outside the correct range for the parameters and/or can require a large amount of iterations to reach convergence. These problems can be aggravated if good starting values are not provided. In this paper we discuss several approaches that can partially avoid these problems providing a good trade-off between efficiency and stability. The performances of these approaches are compared on high-dimensional real and simulated data.
    Journal publication year: 2015
    Publication Type: info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/article

  • Keywords:

    Levenberg-Marquardt algorithm, re-parametrization, starting values, metabolomics data

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