Inference for single and multiple change‐points in time series

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A review of change point detection methods (2018, methodology): “The reader can find more information in the literature, for instance on Bayesian methods [48, 100], an in-depth theoretical survey [72] and numerical comparisons in controlled settings [44].”
Detecting relevant changes in time series models (2014, methodology): “A large portion of the literature attacks the problem of structural breaks by means of hypothesis testing instead of directly focusing on for example estimating the potential break points (see the introduction in Jandhyala et al. (2013)).”
Selective review of offline change point detection methods (2020, background): “As a result, clinear,L1 is often 402 applied on data with noise distributions with heavy tails [8, 25].”
Exact post-selection inference for the generalized lasso path (2018, background): “Hinkley [17], Worsley [42], Bai [2] are some examples, and Jandhyala et al. [20], Horvath and Rice [19] provide nice surveys and extensions.”
Detecting gradual changes in locally stationary processes (2013, background): “An extensive list of references on the localization of abrupt changes can be found in Jandhyala et al. (2014).”