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Then concerning the proposed estimated regression method, both estimated w-Hotelling regressions are able to identify the endpoint window only for the mean.
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Changing the endpoint of this window by ±50 ms did not significantly alter any of the reported findings.
This latter can only identify the window containing endpoint phenomenon.
This proposed method can only determine the window containing endpoint event.
We separated the diabetes endpoint into 12 time windows (every six months for three years and 12 months subsequently).
Open image in new window Figure 1 Endpoints of the 0-, 0.5-, and 1-level sets of fuzzy-valued piecewisequartic polynomials (Example 1).
This clustering was done using the ClusterTree function in the bx-python package (version 0.7.0; http://pypi.python.org/pypi/bx-python, last accessed December 27 , 2013, which provides a data structure for finding clusters of intervals where both endpoints fall within a certain window size.
Identify the window to which the endpoint belongs.
Adopt the latest endpoint OS (specifically, get ready for Windows 10).
Open image in new window Fig. 4 Monitoring endpoint using Linear W-Hotelling regression.
The comparatively long runtime of the endpoint detection stems from the exhaustive sliding window evaluation of each wall.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com