The estimation methods of P-norm distribution is improved in this paper from the perspective of the parameters estimation precision and algorithm complexity. The real order and logarithmic moment estimation is introduced and the real order moment estimation method of P-norm distribution is established based on the actual error distribution. First of all, the relation between the shape parameter p and the real order value r is derived by using the real order moment estimation, and corresponding suggestions are provided for shape parameter's selection. Then, the nonlinear estimation formula of shape parameter, expectations and mean square error is derived via logarithmic moment estimation, function truncation error on the calculation of parameter estimation is eliminated and the solving method of corresponding parameters and calculation process is given, leading an improvement of the theory. Finally, some examples are performed for analyzing the stability and precision of such three methods including real order moment, logarithmic moment and maximum likelihood estimation. The result shows that the stability, precision and convergence speed of the method in this paper are better than maximum likelihood estimation, which generalized the existing errors theory.
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