M-estimator

An M-estimator is a statistical method used to find the best estimate of a parameter (like an average) from data, especially when data may contain outliers or errors. Unlike traditional estimates that give equal importance to all data points, M-estimators assign weights to data based on how well they fit the model, reducing the influence of unusual or incorrect data. This approach makes the estimate more robust and reliable, especially in real-world situations where data imperfections are common. Essentially, M-estimators enhance the accuracy of statistical analysis by automatically adjusting for problematic data points.