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　　Motivated by the need to address these gaps, this study aims to conduct a comprehensive Monte Carlo Comparison of different parameter estimation methods. By simulating data under controlled conditions, we seek to evaluate the strengths and weaknesses of each method and identify factors influencing their performance. This research contributes to the existing literature by providing insights into the relative effectiveness of parameter estimation methods and informing practitioners about the most suitable approaches for their specific research contexts.

　　The structure of this paper is as follows: Section 1 introduces the concept of Monte Carlo Comparison and provides an overview of parameter estimation methods and reviews the current state of research on parameter estimation, highlighting key findings and identifying research gaps. Section 2 presents the methodology employed in this study, including the simulation design and evaluation criteria. Section 3 presents the results of the Monte Carlo Comparison and discusses their implications. Then, section 4 presents innovative designs and modifications to existing parameter estimation methods, followed by a comparison with the methods described above. Finally, Section 5 concludes the paper with a summary of findings, implications for practice, and suggestions for future research directions.

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