# Likelihood Scores for Sparse Signal and Change-Point Detection

@inproceedings{Hu2021LikelihoodSF, title={Likelihood Scores for Sparse Signal and Change-Point Detection}, author={Shouri Hu and Jing-Fu Huang and Hao Chen and Hock Peng Chan}, year={2021} }

We consider here the identiﬁcation of change-points on large-scale data streams. The objective is to ﬁnd the most eﬃcient way of combin-ing information across data stream so that detection is possible under the smallest detectable change magnitude. The challenge comes from the sparsity of change-points when only a small fraction of data streams undergo change at any point in time. The most successful approach to the sparsity issue so far has been the application of hard thresholding such that…

## One Citation

### Optimistic search: Change point estimation for large-scale data via adaptive logarithmic queries

- Computer Science
- 2020

The proposed optimistic search methods with O(logn) evaluations exploiting specific structure of the gain function are optimal for the univariate and multivariate scenarios, and are by far the fastest in the literature under the weakest possible detection condition on the signal-to-noise ratio in the high-dimensional scenario.

## References

SHOWING 1-10 OF 39 REFERENCES

### Optimal multiple change-point detection for high-dimensional data

- Computer Science
- 2020

This manuscript provides a generic algorithm for aggregating local homogeneity tests into an estimator of change-points in a time series and establishes that the error rates of the collection of test directly translate into detection properties of the change-point estimator.

### Optimal sequential detection in multi-stream data

- Computer Science, Mathematics
- 2015

This work shows how the (optimal) detection delay depends on the fraction of data streams undergoing distribution changes as the number of detectors goes to infinity, and shows that the optimal detection delay is achieved by the sum of detectability score transformations of either the partial scores or CUSUM scores of the data streams.

### Sequential multi-sensor change-point detection

- Mathematics2013 Information Theory and Applications Workshop (ITA)
- 2013

We develop a mixture procedure to monitor parallel streams of data for a change-point that affects only a subset of them, without assuming a spatial structure relating the data streams to one…

### High dimensional change point estimation via sparse projection

- Computer Science
- 2016

A two‐stage procedure called inspect is proposed for estimation of the change points, arguing that a good projection direction can be obtained as the leading left singular vector of the matrix that solves a convex optimization problem derived from the cumulative sum transformation of the time series.

### High-dimensional change-point detection under sparse alternatives

- MathematicsThe Annals of Statistics
- 2019

We consider the problem of detecting a change in mean in a sequence of Gaussian vectors. Under the alternative hypothesis, the change occurs only in some subset of the components of the vector. We…

### Optimal detection of multi-sample aligned sparse signals

- Computer Science
- 2015

We describe, in the detection of multi-sample aligned sparse signals, the critical boundary separating detectable from nondetectable signals, and construct tests that achieve optimal detectability:…

### Multiscale change point inference

- Mathematics, Computer Science
- 2013

A new estimator, the simultaneous multiscale change point estimator SMUCE, is introduced, which achieves the optimal detection rate of vanishing signals as n→∞, even for an unbounded number of change points.

### Detecting simultaneous changepoints in multiple sequences.

- Computer ScienceBiometrika
- 2010

It is shown using replicates and parent-child comparisons that pooling data across samples results in more accurate detection of copy number variants and the multisample segmentation algorithm is applied to the analysis of a cohort of tumour samples containing complex nested and overlapping copy number aberrations.

### Stepwise Signal Extraction via Marginal Likelihood

- MathematicsJournal of the American Statistical Association
- 2016

A maximum marginal likelihood estimator is formulated, which can be computed with a quadratic cost using dynamic programming and is applicable to a wide range of models and offers appealing results in practice.

### Optimal and fast detection of spatial clusters with scan statistics

- Computer Science, Mathematics
- 2010

Methodology is introduced that allows for an efficient approximation of the set of all rectangles while still guaranteeing the statistical optimality results described above.