| Summary: | 碩士 === 國立中興大學 === 統計學研究所 === 105 === This study introduces noncentrality and its variant as a measure for clustering longitudinal data,along with an improvement on the computational algorithm:the iterated
recombination of subsets(IRS).Since the IRS algorithm still has considerable burden in computation,our improvement reduces the computation time from K to the nth power to Kn,where K is cluster number and n is the total sample size.
The noncentrality parameter is computed under normality assumption.For longitudinal data,however,this assumption can be violated from time to time.As a remedy,we propose to amend the statistic that was used to estimate the noncentrality by replacing the original observations by the corresponding ranks.For comparisons,we also report the performance of a WSS-index,which was the ”within-cluster sum of squares” based on original observations.We apply our proposal to the clustering of two data sets: (i)Taiwan’s monitoring data of fine particulate matter(PM2.5),and (ii) the nuclear-magnetic-resonance(NMR) data of ”ginseng” with different ages.
|
|---|
