Effects of multicollinearity and autocorrelation on the variable-exponent taper functions

Effects of multicollinearity, autocorrelation and two sampling strategies were studied on two new variable-exponent taper equations using Monte Carlo simulations. These two equations were derived from Kozak's 1988 model. Results of the study indicated that predictions are unbiased even if the taper model has severe multicollinearity and autocorrelation. On the other hand, the estimations are considerably more variable when severe multicollinearity exists. The use of single observations per tree (uncorrelated errors) does not improve the predictive ability of the functions. Taper functions based on a uniform selection of sample trees predict the response variables well, but provide poorer estimates of the parameters than random selection.