Semi-Parametric Methods
Semi-parametric methods are statistical techniques that combine parametric and non-parametric approaches, where part of the model is specified with a finite number of parameters (parametric component) and part is left unspecified (non-parametric component). They offer a flexible middle ground between fully parametric models, which make strong assumptions about data distribution, and fully non-parametric models, which are more data-driven but less efficient. Common examples include Cox proportional hazards models in survival analysis and partially linear models in regression.
Developers should learn semi-parametric methods when working on data analysis tasks where some aspects of the data are well-understood (e.g., linear relationships) but other aspects are unknown or complex, such as in survival analysis, econometrics, or machine learning with censored data. They are particularly useful in scenarios where parametric assumptions might be too restrictive, leading to bias, but non-parametric methods are too computationally intensive or require large sample sizes, such as in medical studies or financial modeling.