Pearson-type goodness-of-fit tests for regression

M. G. Akritas, A. F. Torbeyns

Research output: Contribution to journalArticle

9 Scopus citations

Abstract

A procedure for testing the goodness of fit of linear regression models is introduced. For a given partition of the real line into cells, the proposed test is a quadratic form based on the vector of observed minus expected frequencies of the residuals obtained by maximum-likelihood estimation of the regression parameters. The quadratic form is of the same computational difficulty as the traditional Pearson-type tests with uncensored data. A statistic based on only one cell is particularly easy to apply and is used for testing the normality assumption in a real data set from astronomy. A simulation study examines the finite-sample properties of the proposed tests.

Original languageEnglish (US)
Pages (from-to)359-374
Number of pages16
JournalCanadian Journal of Statistics
Volume25
Issue number3
DOIs
StatePublished - Sep 1997

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Fingerprint Dive into the research topics of 'Pearson-type goodness-of-fit tests for regression'. Together they form a unique fingerprint.

  • Cite this