### Abstract

An important problem in applied statistics is fitting a given model function f(fJ) with unknown parameters fJ to a data vector y . Minimizing the residual sum of squares provides the least squares estimates of p. If fUi) is linear in fJ the precision of these estimates is well· known. In a nonlinear case approximate (thauah asymptotically exact) confidence statements can be made. BEALE [I] introduced measures of nonlinea rity which can be used to indicate when approximate
confidence statements are appropriate. GUTTMAN and MEETER [2] showed that in some. severely
nonlinear. cases Beale's measures do not give the riaht indie-ation. In this paper two new nonlinearity
measures are introduced and their use is illustrated on a practical problem described by WITT [3].
A more detailed discussion of the theoretical background can be found in references [1] and (2).

Original language | English |
---|---|

Pages (from-to) | 93-99 |

Number of pages | 7 |

Journal | Statistica Neerlandica |

Volume | 29 |

Issue number | 3 |

DOIs | |

Publication status | Published - 1975 |

## Fingerprint Dive into the research topics of 'Non-linearity measures : a case study'. Together they form a unique fingerprint.

## Cite this

Linssen, H. N. (1975). Non-linearity measures : a case study.

*Statistica Neerlandica*,*29*(3), 93-99. https://doi.org/10.1111/j.1467-9574.1975.tb00253.x