Maximum Likelihood Estimation in Forest Growth Models with Measurement Errors

Abstract

The forest growth model with measurement errors is introduced. The maximum likelihood estimates (MLE) of the parameters of this model are proven to be consistent and asymptotically normally distributed. The model is applied to the real data from Swedish National Forest Inventory and the MLE of the parameters are obtained

Keywords

Forest growth model; maximum likelihood estimation; random optimization

Published in

Research report (Centre of Biostochastics)
2003, Volume: 2003, number: 3, pages: 1-10 Publisher: SLU

SLU Authors

• Teterukovsky, Alex

  • Department of Forest Economics, Swedish University of Agricultural Sciences
    

• Ranneby, Bo

  • Department of Forest Economics, Swedish University of Agricultural Sciences
    

Permanent link to this page (URI)

https://res.slu.se/id/publ/136