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.
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
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