Minimum-distance estimation

Minimum-distance estimation is a statistical technique used to estimate parameters of a model by minimizing the distance between theoretical predictions and observed data. It is particularly useful when dealing with models where traditional methods, such as maximum likelihood estimation, are difficult to apply or may not yield valid results. Here’s a basic outline of how minimum-distance estimation works: 1. **Distance Metric**: Define a distance metric that quantifies the discrepancy between the observed data and the model's predictions.