A tensor decomposition model for longitudinal microbiome studies Page 1 The Annals of
Applied Statistics 2023, Vol. 17, No. 2, 1105–1126 https://doi.org/10.1214/22-AOAS1661 © …

Motivated by the challenges of analyzing high-dimensional ($ p\gg n $) sequencing data
from longitudinal microbiome studies, where samples are collected at multiple time points …

Tensor is a multiway array. With the rapid development of science and technology in the
past decades, large amount of tensor observations are routinely collected, processed, and …

Background: Distance-based methods for analyzing microbiome data are typically restricted
to testing the global hypothesis of microbiome effect, but do not test the contribution of …

Complex dynamics of microbial communities underlie their essential roles in health and
disease, but our understanding of these dynamics remains incomplete. To bridge this gap …

Motivation Methods for analyzing microbiome data generally fall into one of two groups: tests
of the global hypothesis of any microbiome effect, which do not provide any information on …

Longitudinal'omics analytical methods are extensively used in the evolving field of precision
medicine, by enabling 'big data'recording and high-resolution interpretation of complex …

Tensor decomposition is an important technique for capturing the high-order interactions
among multiway data. Multi-linear tensor composition methods, such as the Tucker …

Abstract Systems serology provides a broad view of humoral immunity by profiling both the
antigen‐binding and Fc properties of antibodies. These studies contain structured …

This paper presents a canonical polyadic (CP) tensor decomposition that addresses
unaligned observations. The mode with unaligned observations is represented using …