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Estimation of Wood Fibre Length Distributions from Censored Data through an EM Algorithm

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  • INGRID SVENSSON
  • SARA SJÖSTEDT‐DE LUNA
  • LENNART BONDESSON

Abstract

. An expectation maximization (EM) algorithm is proposed to find fibre length distributions in standing trees. The available data come from cylindric wood samples (increment cores). The sample contains uncut fibres as well as fibres cut once or twice. The sample contains not only fibres, but also other cells, the so‐called ‘fines’. The lengths are measured by an automatic fibre‐analyser, which is not able to distinguish fines from fibres and cannot tell if a cell has been cut. The data thus come from a censored version of a mixture of the fine and fibre length distributions in the tree. The parameters of the length distributions are estimated by a stochastic version of the EM algorithm, and an estimate of the corresponding covariance matrix is derived. The method is applied to data from northern Sweden. A simulation study is also presented. The method works well for sample sizes commonly obtained from increment cores.

Suggested Citation

  • Ingrid Svensson & Sara Sjöstedt‐De Luna & Lennart Bondesson, 2006. "Estimation of Wood Fibre Length Distributions from Censored Data through an EM Algorithm," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(3), pages 503-522, September.
    • Handle: RePEc:bla:scjsta:v:33:y:2006:i:3:p:503-522
    DOI: 10.1111/j.1467-9469.2006.00501.x
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    Cited by:

    1. Malte Kuhlmann & Claudia Redenbach, 2015. "Estimation of Fibre Length Distributions from Fibre Endpoints," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 1010-1022, December.
    2. L. Sedda & P. Atkinson & E. Barca & G. Passarella, 2012. "Imputing censored data with desirable spatial covariance function properties using simulated annealing," Journal of Geographical Systems, Springer, vol. 14(3), pages 265-282, July.

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