交通大学、清华大学 统计学研究所 专题演讲 题　目：Correlation-Based Functional Clustering via Subspace Projection 主讲人：李百灵教授 (淡江大学统计系) 时　间：98年11月20日(星期五)上午10:40-11:30 (上午10:20-10:40茶会於交大统计所429室举行) 地　点：交大综合一馆427室 Abstract A correlation-based functional clustering method is proposed for grouping curves with similar shapes. A correlation between two random functions defined through the functional inner product is used as similarity measure. Curves with similar shapes are embedded in the cluster subspace spanned by a mean shape function and eigenfunctions of the covariance kernel. The cluster membership prediction for each curve attempts to maximize the functional correlation between the observed and predicted curves via shape standardization and subspace projection among all possible clusters. The proposed method accounts for shape differentials through the functional multiplicative random-effects shape function model for each cluster, which regards random scales and intercept shifts as a nuisance. A consistent estimate is proposed for the random scale effect, whose sample variance estimate is also consistent. The derived identifiability conditions for the clustering procedure unravel the predictability of cluster memberships. Simulation studies and a real data example illustrate the proposed method. --

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