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Discretization-based direct random sample generation

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  • Wang, Liqun
  • Lee, Chel Hee

Abstract

An efficient Monte Carlo method for random sample generation from high dimensional distributions of complex structures is developed. The method is based on random discretization of the sample space and direct inversion of the discretized cumulative distribution function. It requires only the knowledge of the target density function up to a multiplicative constant and applies to standard distributions as well as high-dimensional distributions arising from real data applications. Numerical examples and real data applications are used for illustration. The algorithms are implemented in statistical software R and a package dsample has been developed and is available online.

Suggested Citation

  • Wang, Liqun & Lee, Chel Hee, 2014. "Discretization-based direct random sample generation," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 1001-1010.
    • Handle: RePEc:eee:csdana:v:71:y:2014:i:c:p:1001-1010
    DOI: 10.1016/j.csda.2013.06.011
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    References listed on IDEAS

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    1. Liang, Faming & Liu, Chuanhai & Carroll, Raymond J., 2007. "Stochastic Approximation in Monte Carlo Computation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 305-320, March.
    2. Antonio Punzo & Alessandro Zini, 2012. "Discrete approximations of continuous and mixed measures on a compact interval," Statistical Papers, Springer, vol. 53(3), pages 563-575, August.
    3. James C. Fu & Liqun Wang, 2002. "A Random-Discretization Based Monte Carlo Sampling Method and its Applications," Methodology and Computing in Applied Probability, Springer, vol. 4(1), pages 5-25, March.
    4. Liqun Wang & James Fu, 2007. "A practical sampling approach for a Bayesian mixture model with unknown number of components," Statistical Papers, Springer, vol. 48(4), pages 631-653, October.
    5. Sotto, Cristina & Beunckens, Caroline & Molenberghs, Geert & Kenward, Michael G., 2011. "MCMC-based estimation methods for continuous longitudinal data with non-random (non)-monotone missingness," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 301-311, January.
    6. Bradley P. Carlin & Alan E. Gelfand & Adrian F. M. Smith, 1992. "Hierarchical Bayesian Analysis of Changepoint Problems," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(2), pages 389-405, June.
    7. An, Xinming & Bentler, Peter M., 2012. "Efficient direct sampling MCEM algorithm for latent variable models with binary responses," Computational Statistics & Data Analysis, Elsevier, vol. 56(2), pages 231-244.
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