Efficient multiple change point detection for high‐dimensional generalized linear models

Related Items (1)

Detection and estimation of multiple transient changes

Cites Work

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On asymptotically optimal confidence regions and tests for high-dimensional models
Optimal covariance change point localization in high dimensions
Semiparametric estimation of a binary response model with a change-point due to a covariate threshold
Statistics for high-dimensional data. Methods, theory and applications.
Model selection by LASSO methods in a change-point model
Uniform change point tests in high dimension
Consistencies and rates of convergence of jump-penalized least squares estimators
Econometric issues in the analysis of contagion
Break detection in the covariance structure of multivariate time series models
Simultaneous multiple change-point and factor analysis for high-dimensional time series
Nonconcave penalized likelihood with a diverging number of parameters.
High-dimensional generalized linear models and the lasso
Detection of multiple change-points in multivariate time series
The Dantzig selector: statistical estimation when \(p\) is much larger than \(n\). (With discussions and rejoinder).
SHRINKAGE ESTIMATION OF REGRESSION MODELS WITH MULTIPLE STRUCTURAL CHANGES
Sequential Anomaly Detection in the Presence of Noise and Limited Feedback
Multiscale and multilevel technique for consistent segmentation of nonstationary time series
A test for a change in a parameter occurring at an unknown point
Parallelization of a Common Changepoint Detection Method
A Selective Overview of Variable Selection in High Dimensional Feature Space (Invited Review Article)
Functional Generalized Linear Models with Images as Predictors
Multiple changepoint fitting via quasilikelihood, with application to DNA sequence segmentation
High Dimensional Change Point Estimation via Sparse Projection
Regression Shrinkage and Selection via The Lasso: A Retrospective
Unsupervised Self-Normalized Change-Point Testing for Time Series
Multiple Change-Point Estimation With a Total Variation Penalty
Testing for Threshold Effects in Regression Models
Detection of Changes in Multivariate Time Series With Application to EEG Data
Multiple-Change-Point Detection for High Dimensional Time Series via Sparsified Binary Segmentation
The Lasso for High Dimensional Regression with a Possible Change Point
Multiscale Change Point Inference

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