M-estimators are a broad class of statistical estimators that generalize maximum likelihood estimators. They are defined as solutions to optimization problems, where a specific function, often related to the likelihood or a loss function, is minimized or maximized. This approach allows for flexibility in estimating parameters in various statistical models. These estimators are particularly useful in robust statistics, where they can provide reliable estimates even in the presence of outliers or non-normal data. By focusing on minimizing a loss function, M-estimators can adapt to different types of data and underlying distributions, making them versatile tools in statistical analysis.