Laura Koskela, Bikas K. Sinha, Tapio Nummi. (2007). Some aspects of the sampling distribution of the Apportionment Index and related inference. Silva Fennica vol. 41 no. 4 article id 277. https://doi.org/10.14214/sf.277

Keywords: harvesting; bucking; dissimilarity index; multinomial distribution; overlapping coefficient

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As customer-oriented production strategies have gained ground in the sawmill industry, proper measurement of the fit between the log demand and log output distributions has become of crucial importance. The prevailing means of measuring the outcome is the so-called Apportionment Index (AI), which is calculated from the relative proportions of the observed and required distributions. Although some statistical properties of the AI have recently been examined and alternative means of measuring the bucking outcome have been suggested, properties of the sampling distribution of the AI have not yet been widely studied. In this article we examine the asymptotic sampling distribution for the AI by assuming a multinomial distribution for the outcome. Our results are based mainly on large-sample normal approximations. Also some studies of the determination of the number of logs needed to obtain a specified level of accuracy of the AI have been carried out.

Koskela, University of Tampere, Dept of Mathematics, Statistics and Philosophy, FI-33014 University of Tampere, Finland E-mail: laura.koskela@uta.fi
Sinha, Indian Statistical Institute, Kolkata, India E-mail: bks@nn.in
Nummi, University of Tampere, Dept of Mathematics, Statistics and Philosophy, FI-33014 University of Tampere, Finland E-mail: tn@nn.fi

article id 321, category Research article

Jori Uusitalo, Anne Puustelli, Veli-Pekka Kivinen, Tapio Nummi, Bikas K. Sinha. (2006). Bayesian estimation of diameter distribution during harvesting. Silva Fennica vol. 40 no. 4 article id 321. https://doi.org/10.14214/sf.321

Keywords: diameter distribution; Bayesian statistics; tree harvesting

Abstract | View details | Full text in PDF | Author Info

This research aims to combine two different data sets with Bayesian statistics in order to predict the diameter distribution of trees at harvest. The parameters of prior distribution are derived from the forest management plans supplemented by additional ocular information. We derive the parameters for the sample data from the first trees harvested, and then create the posterior distribution within the Bayesian framework. We apply the standard normal distribution to construct diameter (dbh) distributions, although many other theoretical distributions have been proved better with dbh data available. The methodology developed is then tested on nine mature spruce (Picea abies) dominated stands, on which the normal distribution seems to work well in mature spruce stands. The tests indicate that prediction of diameter distribution for the whole stand based on the first trees harvested is not wise, since it tends to give inaccurate predictions. Combining the first trees harvested with prior information seems to increase the reliability of predictions.

Uusitalo, The Finnish Forest Research Institute, Parkano unit, FI-39700 Parkano, Finland E-mail: jori.uusitalo@metla.fi
Puustelli, University of Tampere, Department of Mathematics, Statistics and Philosophy, FI-33014 University of Tampere, Finland E-mail: ap@nn.fi
Kivinen, University of Helsinki, Department of Forest Resource Management, Box 27, FI-00014 University of Helsinki, Finland E-mail: vpk@nn.fi
Nummi, University of Tampere, Department of Mathematics, Statistics and Philosophy, FI-33014 University of Tampere, Finland E-mail: tn@nn.fi
Sinha, Stat-Math Division, Indian Statistical Institute, 203 B.T. Road, Kolkata - 700 108, India E-mail: bks@nn.in

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