Portmanteau Test of Independence for Functional Observations

By Robertas Gabrys, Piotr Kokoszka. 2007

88 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5e09e60376fec861781515f2c22414baa020a048

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In Statistics and Probability

Citations in the corpus

Detecting changes in the mean of functional observations (2009, methodology): “This can be done by applying the test that was developed by Gabrys and Kokoszka (2007) which is a functional analogue of the well-known test of Hosking (1980) and Li and McLeod (1981) (see also Hosking (1981)).”
Prediction of functional ARMA processes with an application to traffic data (2016, background): “A portmanteau test of Gabrys and Kokoszka [18] applied to X n for n = 1 , . . . , N = 119 working days rejects (with a p -value as small as 10 − 6 ) that the daily functional data are uncorrelated.”
White Noise Testing and Model Diagnostic Checking for Functional Time Series (2016, methodology): “For example, Gabrys and Kokoszka (2007) proposed a portmanteau test for testing the uncorrelatedness of a sequence of functional observations.”
Testing for lack of dependence in the functional linear model (2008, background): “Our asymptotic argument carefully distinguishes between population and estimated functional principal components, a point recently emphasized by Hall & Hosseini-Nasab (2006, 2007).”
Fourier analysis of stationary time series in function space (2013, background): “For sequences of potentially dependent functional data, Gabrys and Kokoszka (2007) and Gabrys, Horváth and Kokoszka (2010) study the detection of correlation.”
Second-Order Comparison of Gaussian Random Functions and the Geometry of DNA Minicircles (2010, methodology): “Second-order tests for functional data analysis pertaining to serial correlation were also investigated (e.g., Gabrys and Kokoszka 2007; Horváth, Hušková, and Kokoszka 2010).”
Testing the structural stability of temporally dependent functional observations and application to climate projections (2011, methodology): “Following the setup in Gabrys and Kokoszka [12], we choose two kernel functions, the Gaussian kernel, ψ(t, s) = C exp ( t + s 2 )”
Dispersion operators and resistant second-order functional data analysis (2012, background): “In a slightly different setting, Gabrys & Kokoszka (2007) and Horváth et al. (2010) investigated second-order tests to detect the presence or change of serial correlation in functional data.”
Applications of functional data analysis: A systematic review (2013, background): “Studies excluded at this stage were mainly those that justified FDA theory rather than presenting examples of its application [8,33-35].”
Determining the order of the functional autoregressive model (2013, methodology): “The transaction volume data set was studied by Laukaitis and Račkauskas (2002), Gabrys and Kokoszka (2007), and Horváth et al.”