Parametric statistics

Parametric statistics is a branch of statistics which leverages models based on a fixed (finite) set of parameters.^[1] Conversely nonparametric statistics does not assume explicit (finite-parametric) mathematical forms for distributions when modeling data. However, it may make some assumptions about that distribution, such as continuity or symmetry, or even an explicit mathematical shape but have a model for a distributional parameter that is not itself finite-parametric.

Most well-known statistical methods are parametric.^[2] Regarding nonparametric (and semiparametric) models, Sir David Cox has said, "These typically involve fewer assumptions of structure and distributional form but usually contain strong assumptions about independencies".^[3]

^ Geisser, S. (2006), Modes of Parametric Statistical Inference, John Wiley & Sons
^ Cox, D. R. (2006), Principles of Statistical Inference, Cambridge University Press
^ Cox 2006, p. 2

[GeisserJohnson-1] Geisser, S. (2006), Modes of Parametric Statistical Inference, John Wiley & Sons

[Cox-2] Cox, D. R. (2006), Principles of Statistical Inference, Cambridge University Press

[3] Cox 2006, p. 2

[1]

[2]

[3]