Detecting changes in the mean of functional observations
Principal component analysis has become a fundamental tool of functional data analysis. It represents the functional data as "X" "i" ("t")="μ"("t")+Σ 1≤"l">&infin ; "η" "i", "l" + "v" "l" ("t "), where "μ" is the common mean, "v" "l" are the eigenfunctions of the covariance operator and the "η" "i", "l" are the scores. Inferential procedures assume that the mean function "μ"("t") is the same for all values of "i". If, in fact, the observations do not come from one population, but rather their mean changes at some point(s), the results of principal component analysis are confounded by the change(s). It is therefore important to develop a methodology to test the assumption of a common functional mean. We develop such a test using quantities which can be readily computed in the R package fda. The null distribution of the test statistic is asymptotically pivotal with a well-known asymptotic distribution. The asymptotic test has excellent finite sample performance. Its application is illustrated on temperature data from England. Copyright (c) 2009 Royal Statistical Society.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 71 (2009)
Issue (Month): 5 ()
|Contact details of provider:|| Postal: 12 Errol Street, London EC1Y 8LX, United Kingdom|
Web page: http://wileyonlinelibrary.com/journal/rssb
More information through EDIRC
|Order Information:||Web: http://ordering.onlinelibrary.wiley.com/subs.asp?ref=1467-9868&doi=10.1111/(ISSN)1467-9868|