Semi-Parametric Estimation
Semi-parametric estimation is a statistical method that combines parametric and non-parametric approaches to model data. It involves specifying a parametric component for certain aspects of the model (e.g., the relationship between variables) while leaving other parts non-parametric (e.g., the error distribution or baseline hazard function). This provides a balance between the flexibility of non-parametric methods and the efficiency and interpretability of parametric models.
Developers should learn semi-parametric estimation when working on data analysis, machine learning, or econometrics projects that require robust modeling with limited assumptions. It is particularly useful in survival analysis (e.g., Cox proportional hazards model), causal inference, and econometric applications where the functional form is partially known but distributions are unknown, allowing for more accurate predictions without overfitting.