604 research outputs found
Spatial CUSUM for Signal Region Detection
Detecting weak clustered signal in spatial data is important but challenging
in applications such as medical image and epidemiology. A more efficient
detection algorithm can provide more precise early warning, and effectively
reduce the decision risk and cost. To date, many methods have been developed to
detect signals with spatial structures. However, most of the existing methods
are either too conservative for weak signals or computationally too intensive.
In this paper, we consider a novel method named Spatial CUSUM (SCUSUM), which
employs the idea of the CUSUM procedure and false discovery rate controlling.
We develop theoretical properties of the method which indicates that
asymptotically SCUSUM can reach high classification accuracy. In the simulation
study, we demonstrate that SCUSUM is sensitive to weak spatial signals. This
new method is applied to a real fMRI dataset as illustration, and more
irregular weak spatial signals are detected in the images compared to some
existing methods, including the conventional FDR, FDR and scan statistics
A Log-Linear Non-Parametric Online Changepoint Detection Algorithm based on Functional Pruning
Online changepoint detection aims to detect anomalies and changes in
real-time in high-frequency data streams, sometimes with limited available
computational resources. This is an important task that is rooted in many
real-world applications, including and not limited to cybersecurity, medicine
and astrophysics. While fast and efficient online algorithms have been recently
introduced, these rely on parametric assumptions which are often violated in
practical applications. Motivated by data streams from the telecommunications
sector, we build a flexible nonparametric approach to detect a change in the
distribution of a sequence. Our procedure, NP-FOCuS, builds a sequential
likelihood ratio test for a change in a set of points of the empirical
cumulative density function of our data. This is achieved by keeping track of
the number of observations above or below those points. Thanks to functional
pruning ideas, NP-FOCuS has a computational cost that is log-linear in the
number of observations and is suitable for high-frequency data streams. In
terms of detection power, NP-FOCuS is seen to outperform current nonparametric
online changepoint techniques in a variety of settings. We demonstrate the
utility of the procedure on both simulated and real data
Sequential Kalman filter for fast online changepoint detection in longitudinal health records
This article introduces the sequential Kalman filter, a computationally
scalable approach for online changepoint detection with temporally correlated
data. The temporal correlation was not considered in the Bayesian online
changepoint detection approach due to the large computational cost. Motivated
by detecting COVID-19 infections for dialysis patients from massive
longitudinal health records with a large number of covariates, we develop a
scalable approach to detect multiple changepoints from correlated data by
sequentially stitching Kalman filters of subsequences to compute the joint
distribution of the observations, which has linear computational complexity
with respect to the number of observations between the last detected
changepoint and the current observation at each time point, without
approximating the likelihood function. Compared to other online changepoint
detection methods, simulated experiments show that our approach is more precise
in detecting single or multiple changes in mean, variance, or correlation for
temporally correlated data. Furthermore, we propose a new way to integrate
classification and changepoint detection approaches that improve the detection
delay and accuracy for detecting COVID-19 infection compared to other
alternatives
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