A New Class of Generalized Probability-Weighted Moment Estimators for the Pareto Distribution

Abstract

Estimation based on probability-weighted moments is a well-established method and an excellent alternative to the classic method of moments or the maximum likelihood method, especially for small sample sizes. In this research, we developed a new class of estimators for the parameters of the Pareto type I distribution. A generalization of the probability-weighted moments approach is the foundation for this new class of estimators. It has the advantage of being valid in the entire parameter space of the Pareto distribution. We established the asymptotic normality of the new estimators and applied them to simulated and real datasets in order to illustrate their finite sample behavior. The results of comparisons with the most used estimation methods were also analyzed.

Effects of Distance and Neighbor Size on Abies hickelii: The Asymmetric Competition Is Aggravated in an Endangered Species

“…To assess whether the five nearest neighbors of A. hickelii were randomly distributed around the reference trees, two analyses were conducted. Firstly, the observed frequency of neighbors around each RT was subjected to the Wald-Wolfowitz randomization test using the "runs.test" function from the "randtests" package in the statistical software R v.3.6.1 [18]. Then, the confidence intervals of the empirical proportions were compared.…”