Maximum-likelihood estimation is a method used in statistics to estimate the parameters of a statistical model. It is based on the principle of finding the values of the model parameters that maximize the likelihood function, which is a measure of how likely the observed data are under the assumed model. In maximum-likelihood estimation, the likelihood function is typically modeled as a function of the parameters of interest, and the goal is to find the values of these parameters that make the observed data most likely. This method is widely used in various fields, including econometrics, biostatistics, and machine learning, among others. Maximum-likelihood estimation is considered a powerful and efficient method for parameter estimation, as it provides estimates that are asymptotically unbiased and have desirable statistical properties. It is also relatively easy to implement and is commonly used in practice due to its simplicity and robustness.