Nonparametrice

Nonparametrice appears to be a misspelling of nonparametric, a term used in statistics and data analysis to describe methods that do not assume a predetermined fixed form for the population distribution or the relationship between variables. Nonparametric methods are characterized by their flexibility, often relying on ranks, order information, or smoothing rather than specific parametric models.

Many nonparametric approaches are distribution-free or make minimal assumptions. Common examples include rank-based tests such as the Mann-Whitney U test, Wilcoxon signed-rank test, and Kruskal-Wallis test, as well as correlation measures like Spearman’s rho and Kendall’s tau. In density and regression tasks, nonparametric methods include kernel density estimation, kernel regression (such as Nadaraya-Watson), and local polynomial regression, which adapt to data without imposing a global functional form. Nonparametric techniques are also widely used in machine learning, including nearest-neighbor methods and certain tree-based algorithms that do not assume a fixed parametric model.

Nonparametric methods are advantageous when the data do not meet the assumptions of parametric models, when sample sizes are small, or when the underlying relationship is complex or unknown. They tend to be robust to outliers and can handle ordinal data effectively. However, they can be less powerful than correctly specified parametric methods when such assumptions hold, may require larger sample sizes, and can involve subjective choices (such as smoothing parameters) that affect results.

In practice, nonparametric methods are chosen for flexibility and robustness, or used as complementary tools alongside parametric or semi-parametric models, which blend fixed-form and flexible components.