Closed-form estimators for the inverse Nakagami distribution.

Area of Science:

• Statistics
• Probability Distributions

Background:

• The inverse Nakagami distribution lacks closed-form maximum likelihood estimators.
• Existing methods present computational challenges.

Purpose of the Study:

• To propose novel closed-form estimators for the inverse Nakagami distribution.
• To develop a bias-corrected version of these estimators.
• To evaluate the performance of the proposed methods.

Main Methods:

• Adaptation of the method of moments for closed-form estimation.
• Derivation of large sample properties and asymptotic variances.
• Comparative analysis using simulation studies and real-world data.

Main Results:

• Successfully derived closed-form estimators for the inverse Nakagami distribution.
• Developed and validated a bias-corrected estimator.
• Demonstrated the performance of proposed estimators against maximum likelihood methods.

Conclusions:

• The proposed method of moments estimators provide a viable alternative to maximum likelihood estimation.
• Bias-corrected estimators offer improved accuracy.
• The findings are supported by simulation and data application results.