Penalized Clustering of Large Scale Functional Data with Multiple Covariates
| dc.creator | Ma, Ping | |
| dc.creator | Zhong, Wenxuan | |
| dc.date | 2008-01-16 | |
| dc.date.accessioned | 2026-07-07T08:54:53Z | |
| dc.date.available | 2026-07-07T08:54:53Z | |
| dc.description | In this article, we propose a penalized clustering method for large scale data with multiple covariates through a functional data approach. In the proposed method, responses and covariates are linked together through nonparametric multivariate functions (fixed effects), which have great flexibility in modeling a variety of function features, such as jump points, branching, and periodicity. Functional ANOVA is employed to further decompose multivariate functions in a reproducing kernel Hilbert space and provide associated notions of main effect and interaction. Parsimonious random effects are used to capture various correlation structures. The mixed-effect models are nested under a general mixture model, in which the heterogeneity of functional data is characterized. We propose a penalized Henderson's likelihood approach for model-fitting and design a rejection-controlled EM algorithm for the estimation. Our method selects smoothing parameters through generalized cross-validation. Furthermore, the Bayesian confidence intervals are used to measure the clustering uncertainty. Simulation studies and real-data examples are presented to investigate the empirical performance of the proposed method. Open-source code is available in the R package MFDA. | |
| dc.identifier | https://arxiv.org/abs/0801.2555 | |
| dc.identifier | http://arxiv.org/abs/0801.2555 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/146103 | |
| dc.subject | Methodology | |
| dc.subject | Computation | |
| dc.title | Penalized Clustering of Large Scale Functional Data with Multiple Covariates | |
| dc.type | text |