Parametric statistics

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Parametric statistics is a branch of statistics. It assumes that in the unknown population, the observations follow a probability distribution. Most of the parameters of the distribution are assumed to be known. Most methods of statistical analysis are of this type.[1] Jacob Wolfowitz was the first to use the term:

Most of these developments have this feature in common, that the distribution functions of the various stochastic variables which enter into their problems are assumed to be of known functional form, and the theories of estimation and of testing hypotheses are theories of estimation of and of testing hypotheses about, one or more parameters. . ., the knowledge of which would completely determine the various distribution functions involved. We shall refer to this situation. . .as the parametric case, and denote the opposite case, where the functional forms of the distributions are unknown, as the non-parametric case.[2]

References[change | change source]

  1. D. R. Cox (2006). Principles of Statistical Inference. Cambridge University Press. ISBN 978-0521685672.
  2. Jacob Wolfowitz (1942). Additive Partition Functions and a Class of Statistical Hypotheses. Annals of Mathematical Statistics. Vol. 13. p. 264.