Testing for changes in the covariance structure of linear processes

By István Berkes, Edit Gombay, Lajos Horv'ath. 2009

82 citations recorded by the source corpus.

Subfields: Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/97fbe5973a6f892efeff6c8e4ba72a4fb524fbc8

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Citations in the corpus

Extensions of some classical methods in change point analysis (2014, methodology): “Introduction and thorough reviews of functional data methods are given in Cuevas (2014), Ferraty and Romain (2011) and Horváth and Kokoszka (2012). One of the first papers on change detection in functional data was motivated by yearly temperature measurements; one curve is constructed from 365 daily”
Structural breaks in time series (2013, methodology): “Early contributions on structural breaks in functional data are Berkes et al. (2009), who developed a functional CUSUM-type test exploiting functional principal components analysis and applied it to temperature records viewing annual profiles as one functional observation.”
Heteroscedasticity and Autocorrelation Robust Structural Change Detection (2013, background): “…ratio test and tests for changes in the spectrum, Inclán and Tiao (1994) and Lee and Park (2001) for change point detection in marginal variance, Berkes, Gombay, and Horváth (2009) and Galeano and Pena (2007) for change detection in the autocovariance function, and Brown, Durbin, and Evans…”
Uniform change point tests in high dimension (2015, background): “[4, 5, 7, 19, 20, 43] and the many references there), the multivariate setup, and especially the high-dimensional case are less often considered.”
Mean shift testing in correlated data (2011, background): “Under dependence assumptions, results involving CUSUMs of observations (similar to results in the i.i.d. case) are discussed in Antoch et al. (1997) and Berkes et al. (2009).”
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)].”
Testing for Change Points in Time Series (2010, background): “…and Inoue (2001) for a change in the marginal distribution function; Picard (1985), Giraitis and Leipus (1992), and Lavielle and Ludena (2000) for a change in the spectrum; Berkes, Gombay, and Horváth (2009), Galeano and Peña (2007) for a change in the autocovariance function at certain lags.”
Changepoints in the North Atlantic Tropical Cyclone Record (2011, unknown): “The CUSUM test of Berkes, Gombay, and Horváth (2009), which allows for autocorrelation, also detects the 1995 changepoint at the 5% signiﬁcance level.”
Detecting relevant changes in time series models (2014, background): “3), which are also called precise hypotheses in the literature (see Berger and Delampady (1987)), has nowadays been widely recognized in various fields of statistical inference including medical, pharmaceutical, chemistry or environmental statistics (see Chow and Liu (1992) and McBride (1999)). Howe”