In order to accurately estimate the positive alpha-stable(PAS)distributed parameters

three kinds of parameter estimate methods

namely ratio

logarithmic moment and iterative logarithmic moment estimate are proposed based on negative-order moments. Ratio of the negative-order moments is directly used for the ratio estimation

and the logarithmic transformation of PAS distribution and Taylor series of the negative-order moment are used to obtain an analytical form of logarithmic moment estimation. Through dividing all samples into different segments

the iterative logarithmic moment estimation is obtained according to iterative computation of data segments. Compared to the conventional estimates

these three estimates achieve much higher estimation accuracy

and the logarithmic moment estimator has lower computing complexity. Monte Carlo simulation results demonstrate that the estimation accuracy of these three estimations can reach 99.8%

99.95%

and 99.94% respectively when the independent running times are 100 and the number of all samples is 5 000.