<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Developing a simplified estimation method without compromising the performance of the distribution is germane to forest modelling. Few estimation methods exist for the Log-Logistic distribution and are relatively complex. A simplified estimator for the Log-Logistic parameters will increase its application in diameter distribution yield systems. Therefore, in this study, a percentile-based estimator was applied for the Log-Logistic distribution. The Kolmogorov-Smirnov, Anderson-Darling and Cramer-von Mises statistics were used to evaluate the method in two natural forest stands and two monospecific plantations of <italic>Gmelina arborea</italic> Roxb. and <italic>Tectona grandis</italic> Linn. f. in Nigeria. The parameter recovery model (PRM) and parameter prediction model (PPM) were used to predict the diameter distributions of independent stands of <italic>G. arborea</italic> and <italic>T. grandis</italic>. The results showed that the percentile estimator did not compromise the quality of fits of the Log-Logistic function across the four forest stands and are comparable to the maximum likelihood estimator. The 25th and 75th, and 40th and 80th were the best sample percentiles for the estimator. The predicted diameter distributions of <italic>G. arborea</italic> and <italic>T. grandis</italic> stands from the PRM and PPM were reasonable and compare well with the observed distribution. Thus, either of the models can be incorporated into the growth and yield system of forest stand management.</p></abstract>

Developing a simplified estimation method without compromising the performance of the distribution is germane to forest modelling. Few estimation methods exist for the Log-Logistic distribution and are relatively complex. A simplified estimator for the Log-Logistic parameters will increase its application in diameter distribution yield systems. Therefore, in this study, a percentile-based estimator was applied for the Log-Logistic distribution. The Kolmogorov-Smirnov, Anderson-Darling and Cramer-von Mises statistics were used to evaluate the method in two natural forest stands and two monospecific plantations of Gmelina arborea Roxb. and Tectona grandis Linn. f. in Nigeria. The parameter recovery model (PRM) and parameter prediction model (PPM) were used to predict the diameter distributions of independent stands of G. arborea and T. grandis. The results showed that the percentile estimator did not compromise the quality of fits of the Log-Logistic function across the four forest stands and are comparable to the maximum likelihood estimator. The 25th and 75th, and 40th and 80th were the best sample percentiles for the estimator. The predicted diameter distributions of G. arborea and T. grandis stands from the PRM and PPM were reasonable and compare well with the observed distribution. Thus, either of the models can be incorporated into the growth and yield system of forest stand management.

A percentile-based estimator for the log-logistic function: Application to forestry

Department of Social and Environmental Forestry

Forestry Studies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Developing a simplified estimation method without compromising the performance of the distribution is germane to forest modelling. Few estimation methods exist...

A percentile-based estimator for the log-logistic function: Application to forestry

Article Category: Research paper

Published Online: Sep 18, 2020

Page range: 107 - 120

Received: Mar 26, 2020

Accepted: Jun 19, 2020

DOI: https://doi.org/10.2478/fsmu-2020-0009

Keywords
percentile method, parameter prediction model,

© 2020 Friday Nwabueze Ogana, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A percentile-based estimator for the log-logistic function: Application to forestry

Article Category: Research paper

Published Online: Sep 18, 2020

Page range: 107 - 120

Received: Mar 26, 2020

Accepted: Jun 19, 2020

DOI: https://doi.org/10.2478/fsmu-2020-0009

Keywordspercentile method, parameter prediction model,

© 2020 Friday Nwabueze Ogana, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
percentile method, parameter prediction model,