Multiple Comparisons Using Composite Likelihood in Clustered Data

Azadbakhsh Mahdis, Gao Xin () and Jankowski Hanna
Additional contact information
Azadbakhsh Mahdis: Department of Statistics and Mathematics, York University, Toronto, ON M3J 1P3, Canada
Gao Xin: Department of Statistics and Mathematics, York University, Toronto, ON M3J 1P3, Canada
Jankowski Hanna: Department of Statistics and Mathematics, York University, Toronto, ON M3J 1P3, Canada

The International Journal of Biostatistics, 2016, vol. 12, issue 2, 12

Abstract: We study the problem of multiple hypothesis testing for correlated clustered data. As the existing multiple comparison procedures based on maximum likelihood estimation could be computationally intensive, we propose to construct multiple comparison procedures based on composite likelihood method. The new test statistics account for the correlation structure within the clusters and are computationally convenient to compute. Simulation studies show that the composite likelihood based procedures maintain good control of the familywise type I error rate in the presence of intra-cluster correlation, whereas ignoring the correlation leads to erratic performance.

Keywords: multiple comparisons; composite likelihood; strong control of type I error (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.1515/ijb-2016-0004 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

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:bpj:ijbist:v:12:y:2016:i:2:p:12:n:13

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/ijb/html

DOI: 10.1515/ijb-2016-0004

Access Statistics for this article

The International Journal of Biostatistics is currently edited by Antoine Chambaz, Alan E. Hubbard and Mark J. van der Laan

More articles in The International Journal of Biostatistics from De Gruyter
Bibliographic data for series maintained by Peter Golla ().