Asymptotic Variance of Newton-Cotes Quadratures based on Randomized Sampling Points

Mads Stehr, Markus Kiderlen

Research output: Working paperResearch


In this paper we consider the problem of numerical integration when sampling nodes are random, and we suggest to use Newton-Cotes quadrature rules to exploit smoothness properties of the integrand. In previous papers it was shown that a Riemann sum approach can cause a severe variance inflation when the sampling points are not equidistant. However, under some integrability conditions on the typical point-distance, we show that Newton-Cotes quadratures based on a stationary point process in R yield unbiased estimators for the integral and that the aforementioned variance inflation can be avoided if a Newton-Cotes quadrature of sufficiently high order is applied. In a stereological application, this corresponds to the estimation of volume of a compact object from area measurements on parallel sections.
Original languageEnglish
Place of PublicationAarhus
PublisherCentre for Stochastic Geometry and Advanced Bioimaging (CSGB), Aarhus University
Number of pages33
Publication statusPublished - 2019
Externally publishedYes
SeriesCSGB Research Reports


  • Point processes
  • Cavalieri estimator
  • Randomized Newton-Cotes quadrature
  • Numerical integration
  • Asymptotic variance bounds

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