Structural Break Estimation for Nonstationary Time Series Models

By Richard A. Davis, Thomas C.M. Lee, G. Rodríguez-Yam. 2006

443 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/b70e67a7535ed137328e329e1a52b7fdde2bfe1b

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In Control and Systems Engineering

In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Optimal detection of changepoints with a linear computational cost (2011, methodology): “This section describes a simulation study that compares the properties of PELT and the genetic algorithm used in Davis et al. (2006) to implement the minimum description length (MDL) test statistic.”
Efficient Bayesian Inference for Multiple Change-Point and Mixture Innovation Models (2008, background): “Traditionally, a majority of articles in this area take a frequentist approach (see Elliott and Müller 2003 for a review and Davis, Lee, and Rodriguez-Yam 2006 for a state-of-the-art algorithm).”
An analysis of global warming in the Alpine region based on nonlinear nonstationary time series models (2012, methodology): “…problems, e.g. autoregressive moving average model fitting (Gaetan 2000), outlier detection (Crawford and Wainwright 1995), variable selection in regression (Chatterjee et al. 1996), the genetic algorithm was suggested specifically for threshold models by Wu and Chang (2002), Davis et al. (2006).”
Detecting Changes in Slope With an L0 Penalty (2017, background): “This covers the common choices of how the penalty depends on segment length (e.g. Zhang and Siegmund, 2007; Davis et al., 2006).”
A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data (2013, methodology): “Lavielle and Teyssiere (2006) analysis is performed by maximizing a log-likelihood function, while Page (1954) examines the ratio of log-likelihood functions to estimate change points. Additionally, Davis et al. (2006) combine the log-likelihood, the minimum description length, and a genetic algorit”
Mean shift testing in correlated data (2011, background): “See Davis et al. (2006) and Lu et al. (2010) for recent work on multiple changepoint problems with autocorrelated data.”
Structural breaks in time series (2013, methodology): “In a piecewise stationary autoregressive time series setting, Davis et al. (2006) proposed an estimator for m0 based on the minimum description length principle (which amounts to selecting a different penalty term dn).”
Group LASSO for Structural Break Time Series (2014, methodology): “The first two sets show that the two-step procedure is comparable to the state-of-the art results of Davis, Lee, and Rodriguez-Yam (2006) in terms of bias and standard error of the estimates.”
Univariate mean change point detection: Penalization, CUSUM and optimality (2018, background): “…structures (e.g. Aue et al., 2009; Avanesov and Buzun, 2016; Wang et al., 2017), in dynamic networks (e.g. Gibberd and Roy, 2017; Wang et al., 2018), and in sequentially-correlated time series (e.g. Lavielle, 1999; Davis et al., 2006; Aue et al., 2009) have been actively studied in recent years.”
Penalized Maximal F Test for Detecting Undocumented Mean Shift without Trend Change (2008, background): “Nevertheless, readers are referred to Caussinus and Mestre (2004) and Davis et al. (2006) for statistical methods that address directly the issue of detecting multiple changepoints in different settings.”
Local Spectral Analysis via a Bayesian Mixture of Smoothing Splines (2009, background): “Although Davis et al. (2006) showed that their simulation results for a few locally (and piecewise) stationary AR models perform better than those of Ombao et al. (2001), it is clear that, generally, a parametric technique will outperform a nonparametric technique when the parametric model is…”
Detecting gradual changes in locally stationary processes (2013, background): “…the mean or the variance of a time series [see Horváth et al. (1999) or Aue et al. (2009)], the parameters of regression models [see Andrews (1993) or Bai and Perron (1998)] or the second order characteristics of a time series [see Berkes et al. (2009), Wied et al. (2012) or Davis et al. (2006)].”
A MOSUM procedure for the estimation of multiple random change points (2018, methodology): “More recently, approaches based on least squares ([44] in context of mean changes or [30] for multivariate regression models), least absolute deviations ([7] for mean and simultaneous variance changes or [3] for linear regression models) or the minimum description length (confer [13] for linear auto”
Heteroscedasticity and Autocorrelation Robust Structural Change Detection (2013, unknown): “See, for instance, Keogh et al. (2001) and Davis, Lee, and Rodriguez-Yam (2006).”
Detection of Multiple Structural Breaks in Multivariate Time Series (2013, methodology): “Finally, we provide a comparison with the procedure suggested by Davis, Lee, and Rodriguez-Yam (2006).”