On the bias and variance in tree volume predictions due to model and measurement errors

The effect of errors in independent variables on the prediction of tree volume is studied. These errors may be either measurement errors, sampling errors, prediction errors or grouping errors. If the model is linear, errors with zero means do not cause bias in predictions, although they do affect the prediction variance. However, if the model is non-linear, errors with zero means cause bias to the predictions. Taylor series expansion, Monte Carlo simulation and recursive modelling are compared in this paper with regard to bias reduction in a simulation experiment. Reasonable bias corrections were obtained with each method. However, if the assumptions about the models do not hold, the corrections may not improve the estimates. The methods selected differ in regard to the assumptions required and the nature of the information used. Thus, the selection of the most preferable method depends on the situation.