TL;DR
WSPCA is presented, a method that incorporates smoothness into the scores of PCA by using a roughness penalty and a leave elements out cross-validation procedure, that for a synthetic data set is capable of estimating the underlying noiseless data.
When collecting intensive longitudinal data in the social, behavioral and biomedical sciences irregularly spaced data (i.e., data that are not collected at equal intervals) are the norm rather than the exception. However, most of the techniques and models that are used to analyze these data in practice were designed fo...
The rapid growth of technologies such as wearable devices and high-throughput sequencing has generated unprecedented volumes of longitudinal data, often with multiple correlated features. This trend highlights the need for robust clustering methods tailored to multi-feature longitudinal datasets. Although previous revi...
We study structural-change testing in multivariate linear regression when the response dimension is proportional to the sample size and the number of predictors is fixed. Despite its relevance to applications across a broad range of fields, this problem remains underexplored. The alternatives of interest allow multiple...
This work proposes Cooperative Learning with a penalized Linear Mixed Model (CL-pLMM) for high-dimensional multiview data with a clustered structure and shows that its objective function can be represented as a penalized linear mixed-effects model applied to augmented data, allowing existing estimation procedures to be...
This chapter presents tensor decomposition (TD)-based unsupervised feature extraction (FE) as a robust framework for feature selection in bioinformatics and provides a versatile and effective approach for extracting meaningful biological insights from complex, high-dimensional datasets.
Longitudinal data modeling attracts special interest from researchers and practitioners because of its capacity to help understand individual and mean trends of growth and development over time. One feature of longitudinal design is the collection of extensive information on participants to help explain the underlying...
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