Kimesurface Representation and Tensor Linear Modeling of Longitudinal Data.

ABSTRACT: Many modern techniques for analyzing time-varying longitudinal data rely on parametric models to interrogate the time-courses of univariate or multivariate processes. Typical analytic objectives include utilizing retrospective observations to model current trends, predict prospective trajectories, derive categorical traits, or characterize various relations. Among the many mathematical, statistical, and computational strategies for analyzing longitudinal data, tensor-based linear modeling offers a unique algebraic approach that encodes different characterizations of the observed measurements in terms of state indices. This paper introduces a new method of representing, modeling, and analyzing repeated-measurement longitudinal data using a generalization of event order from the positive rea

SUBMITTER: Zhang R 

PROVIDER: S-EPMC9355340 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature