Rayleigh Regression

Rayleigh regression, a statistical model used to analyze data following a Rayleigh distribution, finds applications in diverse fields like image processing and geophysical analysis. Current research focuses on improving the robustness and accuracy of Rayleigh regression, particularly addressing challenges posed by outliers and small sample sizes, often employing techniques like weighted maximum likelihood estimation and bias-adjusted estimators. Furthermore, deep learning architectures, such as convolutional neural networks (CNNs) and physics-informed neural networks (PINNs), are increasingly used to enhance the efficiency and precision of Rayleigh-based analyses, particularly in applications involving complex data like seismic records and exoplanetary atmospheric modeling. These advancements contribute to more accurate and reliable inferences from Rayleigh-distributed data across various scientific disciplines.