Evaluation of three lack of fit tests in linear regression models

Daniel Wang and Michael Conerly

Journal of Applied Statistics, 2003, vol. 30, issue 6, 683-696

Abstract: A key diagnostic in the analysis of linear regression models is whether the fitted model is appropriate for the observed data. The classical lack of fit test is used for testing the adequacy of a linear regression model when replicates are available. While many efforts have been made in finding alternative lack of fit tests for models without replicates, this paper focuses on studying the efficacy of three tests: the classical lack of fit test, Utts' (1982) test, Burn & Ryan's (1983) test. The powers of these tests are computed for a variety of situations. Comments and conclusions on the overall performance of these tests are made, including recommendations for future studies.

Date: 2003
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.tandfonline.com/doi/abs/10.1080/0266476032000053763 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:30:y:2003:i:6:p:683-696

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/0266476032000053763

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().