Asymptotic distribution of score statistics for spatial cluster detection with censored data

Commenges, Daniel and Liquet, Benoit (2008) Asymptotic distribution of score statistics for spatial cluster detection with censored data. Biometrics, 64 4: 1287-1292. doi:10.1111/j.1541-0420.2008.01132.x


Author Commenges, Daniel
Liquet, Benoit
Title Asymptotic distribution of score statistics for spatial cluster detection with censored data
Journal name Biometrics   Check publisher's open access policy
ISSN 0006-341X
1541-0420
Publication date 2008-12-01
Year available 2008
Sub-type Article (original research)
DOI 10.1111/j.1541-0420.2008.01132.x
Open Access Status Not Open Access
Volume 64
Issue 4
Start page 1287
End page 1292
Total pages 6
Place of publication Chichester, West Sussex, United Kingdom
Publisher Wiley-Blackwell Publishing
Language eng
Formatted abstract
Cook, Gold, and Li (2007, Biometrics 63, 540-549) extended the Kulldorff (1997, Communications in Statistics 26, 1481-1496) scan statistic for spatial cluster detection to survival-type observations. Their approach was based on the score statistic and they proposed a permutation distribution for the maximum of score tests. The score statistic makes it possible to apply the scan statistic idea to models including explanatory variables. However, we show that the permutation distribution requires strong assumptions of independence between potential cluster and both censoring and explanatory variables. In contrast, we present an approach using the asymptotic distribution of the maximum of score statistics in a manner not requiring these assumptions.
Keyword Asymptotic distribution
Cluster detection
Generalized linear model
Permutation test
Score test
Spatial scan statistic
Survival data
Tests
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Non-UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: School of Mathematics and Physics
 
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Created: Sat, 14 Sep 2013, 02:41:47 EST by Kay Mackie on behalf of School of Mathematics & Physics