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Multiscale Change Point Inference [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2014 SummaryWe introduce a new estimator, the simultaneous multiscale change point estimator SMUCE, for the change point problem in exponential family regression. An unknown step function is estimated by minimizing the number of change points over the acceptance region of a multiscale test at a level α.
Frick, K., Munk, A., Sieling, H.
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Algebraic change-point detection [PDF]
Applicable Algebra in Engineering, Communication and Computing, 2010 Elementary techniques from operational calculus, differential algebra, and noncommutative algebra lead to a new approach for change-point detection, which is an important field of investigation in various areas of applied sciences and engineering. Several successful numerical experiments are presented.
Fliess, Michel +2 more
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Heterogeneous Change Point Inference [PDF]
Journal of the Royal Statistical Society Series B: Statistical Methodology, 2016 Summary We propose, a heterogeneous simultaneous multiscale change point estimator called ‘H-SMUCE’ for the detection of multiple change points of the signal in a heterogeneous Gaussian regression model. A piecewise constant function is estimated by minimizing the number of change points over the acceptance region of a multiscale test ...
Pein, F., Sieling, H., Munk, A.
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Graph-based change-point detection [PDF]
The Annals of Statistics, 2015 We consider the testing and estimation of change-points -- locations where the distribution abruptly changes -- in a data sequence. A new approach, based on scan statistics utilizing graphs representing the similarity between observations, is proposed.
Chen, Hao, Zhang, Nancy
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A change point-based analysis procedure for improving the success rate of decision-making in clinical trials with delayed treatment effects [PDF]
Frontiers in Pharmacology, 2023 A delayed treatment effect is a commonly observed phenomenon in tumor immunotherapy clinical trials. It can cause a loss of statistical power and complicate the interpretation of the analytical findings.
Long-Shen Xie, Hui Lu
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Detecting Structural Change Point in ARMA Models via Neural Network Regression and LSCUSUM Methods [PDF]
Entropy, 2023 This study considers the change point testing problem in autoregressive moving average (ARMA) (p,q) models through the location and scale-based cumulative sum (LSCUSUM) method combined with neural network regression (NNR).
Xi-hame Ri, Zhanshou Chen, Yan Liang
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Quantifying the contributions of climate change and human interventions on streamflow alteration in the Hableroud River basin using the hydrological sensitivity analysis approach based on the Budyko hypothesis [PDF]
مدلسازی و مدیریت آب و خاک, 2023 IntroductionClimate change and human interventions are the most important factors that in combination influence the hydrological response of a watershed system.
Vahedberdi Sheikh +6 more
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Optimal change point detection and localization in sparse dynamic networks [PDF]
, 2021 We study the problem of change point localization in dynamic networks models. We assume that we observe a sequence of independent adjacency matrices of the same size, each corresponding to a realization of an unknown inhomogeneous Bernoulli model.
Rinaldo, Alessandro, Wang, Daren, Yu, Yi
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Microorganisms, 2022 Diatom communities significantly influence ocean primary productivity and carbon cycling, but their spatial and temporal dynamics are highly heterogeneous and are governed by a complex diverse suite of abiotic and biotic factors. We examined the seasonal
Nine Le Reun +9 more
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Poisson regression model with change points [PDF]
Journal of Applied Research on Industrial Engineering, 2021 There are many different fields the change point analysis arises. In those cases, the main problem is locating the unknown change points. The aim of this study is to detect location and time of change point in Poisson regression model.
Reza Habibi
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