Roughness regularization for functional data analysis with free knots spline estimation
<|reference_start|>Roughness regularization for functional data analysis with free knots spline estimation: In the era of big data, an ever-growing volume of information is recorded, either continuously over time or sporadically, at distinct time intervals. Functional Data Analysis (FDA) stands at the cutting edge of this data revolution, offering a powerful framework for handling and extracting meaningful insights from such complex datasets. The currently proposed FDA me\-thods can often encounter challenges, especially when dealing with curves of varying shapes. This can largely be attributed to the method's strong dependence on data approximation as a key aspect of the analysis process. In this work, we propose a free knots spline estimation method for functional data with two penalty terms and demonstrate its performance by comparing the results of several clustering methods on simulated and real data.<|reference_end|>
@article{de magistris2024roughness,
    title={Roughness regularization for functional data analysis with free knots
  spline estimation},
    author={Anna De Magistris (1), Valentina De Simone (1), Elvira Romano (1) and
  Gerardo Toraldo (1) ((1) University of Campania "Luigi Vanvitelli")},
    journal={arXiv preprint arXiv:2407.05159},
    year={2024},
    doi={10.1007/s11222-024-10474-w},
    archivePrefix={arXiv},
    eprint={2407.05159},
    primaryClass={stat.ME cs.NA math.NA stat.AP}
}