L-moments (Python)
The link for the description of how to use the model is below:
This Python script provides a robust and efficient solution for fitting statistical distributions to data using L-Moments. L-Moments are a powerful tool for statistical analysis, offering greater resilience to outliers and skewed data compared to traditional moments. This script leverages the versatility of L-Moments to accurately model a wide range of probability distributions, making it an essential tool for statisticians, data scientists, and researchers.
The tool offers high accuracy through precise L-Moments calculations and fitting algorithms, complemented by visualization tools for model validation and interpretation. Usage involves preparing your dataset, calculating L-Moments, fitting distributions, evaluating model fit with diagnostic plots, and generating visual comparisons to empirical data.
Key Features:
- Comprehensive Distribution Fitting: Supports a variety of distributions including Generalized Extreme Value, Generalized Logistic, Generalized Pareto, Gumbel, and Pearson III.
- Enhanced Robustness: L-Moments provide superior resistance to outliers, ensuring more reliable parameter estimation for skewed or heavy-tailed distributions.
- Automated Workflow: Streamlined process for calculating L-Moments and fitting distributions, with minimal user intervention required.
- High Accuracy: Implements precise algorithms to calculate L-Moments and fit distributions, ensuring high accuracy in modeling real-world data.
- Visualization Tools: Integrated plotting functions to visualize the fitted distributions against empirical data, aiding in model validation and interpretation.
Usage:
- Data Preparation: Input your dataset in a compatible format (e.g., NumPy array, Pandas DataFrame).
- L-Moments Calculation: Utilize the provided functions to compute L-Moments from your data.
- Distribution Fitting: Fit various distributions to the data using the computed L-Moments.
- Model Evaluation: Assess the goodness-of-fit with diagnostic plots and statistical measures.
- Visualization: Generate visual representations of the fitted distributions to compare against your empirical data.
You'll get a comprehensive tool for fitting distributions using L-Moments, featuring robust support for extreme value distributions such as GEV, GLO (Generalized Logistic), GPA (Generalized Pareto), and Gumbel. Enhanced robustness ensures reliable parameter estimation even for skewed or heavy-tailed distributions, while an automated workflow simplifies the process with minimal user intervention.