Corpus ID: 207847426

Normal variance mixtures: Distribution, density and parameter estimation

@article{Hintz2019NormalVM,
  title={Normal variance mixtures: Distribution, density and parameter estimation},
  author={Erik Hintz and M. Hofert and C. Lemieux},
  journal={arXiv: Computation},
  year={2019}
}
  • Erik Hintz, M. Hofert, C. Lemieux
  • Published 2019
  • Mathematics
  • arXiv: Computation
  • Efficient computation of the distribution and log-density function of multivariate normal variance mixtures as well as a likelihood-based fitting procedure for those distributions are presented. Existing methods for the evaluation of the distribution function of a multivariate normal and t distribution are generalized to an efficient randomized quasi-Monte Carlo (RQMC) algorithm that is able to estimate the probability that a random vector following a multivariate normal variance mixture… CONTINUE READING

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    References

    SHOWING 1-10 OF 11 REFERENCES
    ML ESTIMATION OF THE t DISTRIBUTION USING EM AND ITS EXTENSIONS, ECM AND ECME
    • 245
    • Highly Influential
    • PDF
    The effective dimension and quasi-Monte Carlo integration
    • 175
    The ECME algorithm: A simple extension of EM and ECM with faster monotone convergence
    • 517
    • Highly Influential
    Monte Carlo and Quasi-Monte Carlo Sampling
    • 426
    • PDF
    Global Sensitivity Analysis
    • H. Wagner
    • Mathematics, Computer Science
    • Oper. Res.
    • 1995
    • 765
    Better estimation of small sobol' sensitivity indices
    • A. Owen
    • Mathematics, Computer Science
    • TOMC
    • 2013
    • 85
    • Highly Influential
    • PDF
    Quantitative Risk Management: Concepts
    • 306
    (Randomized) Quasi-Random Number Generators
    • 3
    QRM: Provides R-Language Code to Examine Quantitative
    • 2016
    Remarks on a Multivariate Transformation
    • 2,268
    • Highly Influential