Parameter Estimation for Generalized Gaussian Distribution Implementation

This MATLAB program provides significant utility by enabling parameter estimation for Generalized Gaussian Distributions (GGD). GGD finds extensive applications across multiple domains including finance, medical research, and engineering. The implementation employs maximum likelihood estimation (MLE) methods to accurately determine shape and scale parameters from input data samples. Through this program, users can gain deeper insights into GGD characteristics such as tail behavior and kurtosis properties, facilitating more effective application in practical problem-solving scenarios. The code architecture offers several advantages including rapid computation through optimized numerical algorithms, high estimation accuracy with convergence checks, and customizable configuration options for specific research requirements. Key features include: - Automated data preprocessing and validation routines - Iterative optimization using Newton-Raphson or Fisher scoring methods - Confidence interval calculation for parameter estimates - Visualization tools for distribution fitting assessment For researchers and professionals working with Generalized Gaussian Distributions, this program serves as a reliable computational tool that balances computational efficiency with statistical rigor. The modular design allows straightforward integration with existing MATLAB workflows and supports various data input formats.