A Nonparametric Relative Entropy Method for Detecting Complexity Changes in Intermittent Time Series
This paper introduces a novel nonparametric relative entropy (RlEn) method for detecting changes in complexity within intermittent time series data, demonstrating its superior performance over existing methods like ApEn through simulations and a real-world application in analyzing human motor output complexity during fatigue.
Multiple Change-Point Detection in Point Processes: An Exact Optimization Approach Using Cross-Validation
This paper proposes an efficient and exact method for detecting multiple change-points in point processes, particularly inhomogeneous Poisson and marked Poisson processes, using a minimum contrast estimator, dynamic programming, and a novel cross-validation procedure.
Optimal Change-Point Testing for High-Dimensional Linear Models with Temporal Dependence (No Change)
This paper introduces QF-CUSUM, a novel statistical test designed to detect change-points in high-dimensional linear models with temporally dependent data, demonstrating its optimality, robustness, and practical utility through theoretical analysis and simulations.
A Novel Max-EM Algorithm for Change-Point Detection in Regression Models for Ordered Data
This paper introduces the max-EM algorithm, a novel method for detecting breakpoints (change-points) in ordered data within a regression modeling framework, demonstrating its effectiveness through simulations and real-world applications.
Break Recovery in Time-Varying Sparse Precision Matrices Using Group Fused D-trace LASSO
This research paper introduces a novel method called Group Fused D-trace LASSO (GFDtL) for detecting structural breaks in time-varying networks by estimating the sparse precision matrix, which is assumed to change in a piece-wise constant manner.
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