Hot-spots detection in count data by Poisson assisted smooth sparse tensor decomposition
Count data occur widely in many bio-surveillance and healthcare applications, eg the
numbers of new patients of different types of infectious diseases from different …
numbers of new patients of different types of infectious diseases from different …
Rapid detection of hot-spot by tensor decomposition on space and circular time with application to weekly gonorrhea data
In many bio-surveillance and healthcare applications, data sources are measured from
many spatial locations repeatedly over time, say, daily/weekly/monthly. In these applications …
many spatial locations repeatedly over time, say, daily/weekly/monthly. In these applications …
Rapid detection of hot-spot by tensor decomposition with application to weekly gonorrhea data
In many bio-surveillance and healthcare applications, data sources are measured from
many spatial locations repeatedly over time, say, daily/weekly/monthly. In these applications …
many spatial locations repeatedly over time, say, daily/weekly/monthly. In these applications …
Rapid detection of hot-spots via tensor decomposition with applications to crime rate data
In many real-world applications of monitoring multivariate spatio-temporal data that are non-
stationary over time, one is often interested in detecting hot-spots with spatial sparsity and …
stationary over time, one is often interested in detecting hot-spots with spatial sparsity and …
Cluster-based analysis of infectious disease occurrences using tensor decomposition: a case study of South Korea
For a long time, various epidemics, such as lower respiratory infections and diarrheal
diseases, have caused serious social losses and costs. Various methods for analyzing …
diseases, have caused serious social losses and costs. Various methods for analyzing …
New progress in hot-spots detection, partial-differential-equation-based model identification and statistical computation
Y Zhao - 2021 - repository.gatech.edu
This thesis discusses the new progress in (1) hot-spots detection in spatial-temporal data,(2)
partial-differential-equation-based (PDE-based) model identification, and (3) optimization in …
partial-differential-equation-based (PDE-based) model identification, and (3) optimization in …
Optimal sparse singular value decomposition for high-dimensional high-order data
In this article, we consider the sparse tensor singular value decomposition, which aims for
dimension reduction on high-dimensional high-order data with certain sparsity structure. A …
dimension reduction on high-dimensional high-order data with certain sparsity structure. A …
Tensor decomposition for infectious disease incidence data
H Korevaar, CJ Metcalf… - Methods in ecology and …, 2020 - Wiley Online Library
Many demographic and ecological processes generate seasonal and other periodicities.
Seasonality in infectious disease transmission can result from climatic forces such as …
Seasonality in infectious disease transmission can result from climatic forces such as …
Modeling the positive testing rate of COVID-19 in South Africa using a semi-parametric smoother for binomial data
OE Owokotomo, S Manda, J Cleasen… - Frontiers in Public …, 2023 - frontiersin.org
Identification and isolation of COVID-19 infected persons plays a significant role in the
control of COVID-19 pandemic. A country's COVID-19 positive testing rate is useful in …
control of COVID-19 pandemic. A country's COVID-19 positive testing rate is useful in …
Tensor decomposition with generalized lasso penalties
OH Madrid-Padilla, J Scott - Journal of Computational and …, 2017 - Taylor & Francis
We present an approach for penalized tensor decomposition (PTD) that estimates smoothly
varying latent factors in multiway data. This generalizes existing work on sparse tensor …
varying latent factors in multiway data. This generalizes existing work on sparse tensor …