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　　\section{Future Research}
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　　The Pearson Type III distribution is not a commonly encountered probability distribution. In this study, we utilized only two methods, namely, method of moments and maximum likelihood estimation, to estimate its three parameters. From the comparative analysis results, it can be observed that the selection of parameter values also affects the parameter estimation outcomes. This is because different parameter settings can lead to varying probability distribution shapes and characteristics, thereby influencing the accuracy and bias of parameter estimation. Therefore, in future research, we can further analyze the relationship between parameter settings and the accuracy of estimation results.

　　Additionally, we can explore the use of uncommon parameter estimation methods for estimating the three parameters of the Pearson Type III distribution, such as Minimum Chi-Square Estimation.\\

　　\textbf{Overview of Minimum Chi-Square Estimation}\\

　　\textbf{Origin and Development:} The Minimum Chi-Square Estimation method originated in the early 20th century, evolving from Karl Pearson's work on goodness-of-fit tests. It was further developed by Neyman and Pearson as a method for parameter estimation in statistical modeling[22].\\

　　\textbf{Applicability: }Minimum Chi-Square Estimation is widely used for fitting discrete probability distributions to data, such as the Poisson distribution or multinomial distribution [23]. It is also used for contingency table analysis and in logistic regression for binary outcomes [24]. This method is particularly suitable for discrete data or data that can be categorized into distinct groups. It is commonly used in fields such as epidemiology, social sciences, and biology to estimate parameters of categorical data models [22,23].\\

　　\textbf{Limitations: }Minimum Chi-Square Estimation assumes that the observed data follows a specific theoretical distribution, which may not always hold true in real-world scenarios [24]. It is also sensitive to the choice of initial parameter values and may not always converge to the global minimum.\\

　　Here is the mathematical formula for Minimum Chi-Square Estimation along with explanations of the parameters:
　　The formula for Minimum Chi-Square Estimation is:\\