Linear Model

Linear models, fundamental tools in statistical modeling and machine learning, aim to establish linear relationships between variables for prediction and inference. Current research emphasizes extensions beyond standard assumptions (e.g., non-Gaussian data, high dimensionality), exploring robust methods like stochastic gradient descent, dropout regularization, and various regularization techniques to improve generalization and handle noisy or incomplete data. These advancements are crucial for addressing challenges in diverse fields, including causal inference, time series forecasting, and high-dimensional data analysis where traditional linear models may fall short.