Detecting changes in the mean of functional observations

By István Berkes, Robertas Gabrys, Lajos Horv'ath, Piotr Kokoszka. 2009

196 citations recorded by the source corpus.

Subfields: Food Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/7e029a4d60e6cb9a87856f77d67a0308453d3c80

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Citations in the corpus

Testing relevant hypotheses in functional time series via self‐normalization (2018, methodology): “2 in Berkes et al. (2013). For i 6= j, one can use similar arguments as in the proof of the latter Lemma.”
Locally Stationary Functional Time Series (2016, background): “Violation of the assumption of identically distributed observations has been examined in the setting of change-point detection (e.g. Berkes et al., 2009; Hörmann and Kokoszka, 2010; Aue et al., 2009; Horváth, Hušková and Kokoszka, 2010; Gabrys, Hörmann and Kokoszka, 2010), in the context of…”
Uniform change point tests in high dimension (2015, result): “[6, 26, 30]), the literature in the latter case is rather sparse compared to the univariate theory.”
Finite sample change point inference and identification for high‐dimensional mean vectors (2017, methodology): “…CUSUM statistics for fixed sampled size tests, as in many other statistical change point testing and estimation works (Aue et al., 2009; Bai, 1997; Berkes et al., 2009; Bhattacharya, 1987; Brodsky & Darkhovsky, 1993; Carlstein, 1988; Csörgo˝ & Horváth, 1997; Frick et al., 2014; Fryzlewicz,…”
Sequential block bootstrap in a Hilbert space with application to change point analysis (2014, methodology): “They are displayed in Tables 1 and 2, together with the original results of Berkes et al. (2009). The performances of Table 1: Empirical size for bootstrapped test (T ∗ n,p) and the fpca-based test (Sn,d); nominal size α = 0.”
Functional data analysis in the Banach space of continuous functions (2017, background): “[7] Istvan Berkes, Robertas Gabrys, Lajos Horváth, and Piotr Kokoszka.”
Dynamic functional principal components (2012, background): “[4] consider the problem of detecting a change in the mean function of a sequence of independent functional data.”
Testing the structural stability of temporally dependent functional observations and application to climate projections (2011, methodology): “Testing for a change point in the mean of functional data has been studied in Berkes, Gabrys, Horváth and Kokoszka [4], but their test was developed under the independence assumption.”
Second-Order Comparison of Gaussian Random Functions and the Geometry of DNA Minicircles (2010, methodology): “To probe this finding more formally, we test the hypothesis of equal mean curves versus a general alternative, based on a variant of the test proposed by Berkes et al. (2009).”
Wasserstein autoregressive models for density time series (2020, methodology): “Research of this type has been done for linear functional time series, see, for example, Berkes et al. (2009), Horváth et al.”
Testing stationarity of functional time series (2014, methodology): “j=i+1 (μj−i(s)− μ̄N(s))   = O(1), and by Berkes et al. (2013)”
Extensions of some classical methods in change point analysis (2014, methodology): “Results establishing (3.3) under various dependence conditions can be found in Berkes et al. (2008, 2009a), Berkes and Horváth (2001, 2003), Berkes and Philipp (1977), Billingsley (1968), Louhichi (2000), Shao and Yu (1996) and Yu (1993).”