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General Purpose Convolution Algorithm in S4 Classes by Means of FFT

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  • Ruckdeschel, Peter
  • Kohl, Matthias

Abstract

Object orientation provides a flexible framework for the implementation of the convolution of arbitrary distributions of real-valued random variables. We discuss an algorithm which is based on the fast Fourier transform. It directly applies to lattice-supported distributions. In the case of continuous distributions an additional discretization to a linear lattice is necessary and the resulting lattice-supported distributions are suitably smoothed after convolution. We compare our algorithm to other approaches aiming at a similar generality as to accuracy and speed. In situations where the exact results are known, several checks confirm a high accuracy of the proposed algorithm which is also illustrated for approximations of non-central χ2 distributions. By means of object orientation this default algorithm is overloaded by more specific algorithms where possible, in particular where explicit convolution formulae are available. Our focus is on R package distr which implements this approach, overloading operator + for convolution; based on this convolution, we define a whole arithmetics of mathematical operations acting on distribution objects, comprising operators +, -, *, /, and ^.

Suggested Citation

  • Ruckdeschel, Peter & Kohl, Matthias, 2014. "General Purpose Convolution Algorithm in S4 Classes by Means of FFT," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 59(i04).
    • Handle: RePEc:jss:jstsof:v:059:i04
    DOI: http://hdl.handle.net/10.18637/jss.v059.i04
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    References listed on IDEAS

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    1. Warnes, Gregory R., 2002. "HYDRA: a Java library for Markov Chain Monte Carlo," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 7(i04).
    2. Grübel, Rudolf & Hermesmeier, Renate, 1999. "Computation of Compound Distributions I: Aliasing Errors and Exponential Tilting," ASTIN Bulletin, Cambridge University Press, vol. 29(2), pages 197-214, November.
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    1. Biscarri, William & Zhao, Sihai Dave & Brunner, Robert J., 2018. "A simple and fast method for computing the Poisson binomial distribution function," Computational Statistics & Data Analysis, Elsevier, vol. 122(C), pages 92-100.
    2. Valeriy A. Naumov & Yuliya V. Gaidamaka & Konstantin E. Samouylov, 2020. "Computing the Stationary Distribution of Queueing Systems with Random Resource Requirements via Fast Fourier Transform," Mathematics, MDPI, vol. 8(5), pages 1-9, May.
    3. Jenq-Tzong Shiau, 2021. "Analytical Water Shortage Probabilities and Distributions of Various Lead Times for a Water Supply Reservoir," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(11), pages 3809-3825, September.
    4. Taro Ohdoko & Satoru Komatsu, 2023. "Integrating a Pareto-Distributed Scale into the Mixed Logit Model: A Mathematical Concept," Mathematics, MDPI, vol. 11(23), pages 1-22, November.
    5. Richard L. Warr & Cason J. Wight, 2020. "Error Bounds for Cumulative Distribution Functions of Convolutions via the Discrete Fourier Transform," Methodology and Computing in Applied Probability, Springer, vol. 22(3), pages 881-904, September.

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