Concurrent object regression
نویسندگان
چکیده
Modern-day problems in statistics often face the challenge of exploring and analyzing complex non-Euclidean object data that do not conform to vector space structures or operations. Examples such objects include covariance matrices, graph Laplacians networks, univariate probability distribution functions. In current contribution a new concurrent regression model is proposed characterize time-varying relation between an general metric (as response) Rp predictor), where concepts from Fréchet employed. Concurrent has been well-developed area research for Euclidean predictors responses, with many important applications longitudinal studies functional data. However, there no available so far as responses. We develop generalized versions both global least squares locally weighted smoothing context responses which are situated spaces propose estimators can accommodate sparse and/or irregular designs. Consistency results demonstrated sample estimates appropriate population targets along corresponding rates convergence. The models illustrated human mortality resting state Magnetic Resonance Imaging (fMRI)
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ژورنال
عنوان ژورنال: Electronic Journal of Statistics
سال: 2022
ISSN: ['1935-7524']
DOI: https://doi.org/10.1214/22-ejs2040