Inference for Functional Data with Applications

By L. Horvath, Piotr Kokoszka. 2012

1,386 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/82d90dcf62d17e50c6e7b116b18054d8bd386afe

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Citations in the corpus

Tests for separability in nonparametric covariance operators of random surfaces (2015, methodology): “Many applications involve hypersurface data, data that is both functional (as in functional data analysis, see e.g. Ramsay & Silverman 2005, Ferraty & Vieu 2006, Horváth & Kokoszka 2012a, Wang et al. 2015) and multidimensional.”
Testing stationarity of functional time series (2014, methodology): “(2012) and Kokoszka et al. (2013), for bootstrap and resampling methods for functional time series, see McMurry and Politis (2010) and for the functional analysis of volatility, see Müller et al. (2011). Testing stationarity received due attention as soon as fundamental time series modeling principl”
Distances and inference for covariance operators (2014, methodology): “In recent years, many statistical techniques have been proposed to deal with functional data (see, e.g., Ramsay & Silverman, 2005; Ferraty & Vieu, 2006; Horváth & Kokoszka, 2012).”
On the optimal reconstruction of partially observed functional data (2017, background): “Note that the KL representation provides the very basis of a majority of the works in functional data analysis (cf. Ramsay and Silverman, 2005; Horváth and Kokoszka, 2012).”
Prediction of functional ARMA processes with an application to traffic data (2016, methodology): “The dimension reduction based on the principal components is optimal for un-correlated data in terms of its L 2 -accuracy (cf. Horv`ath and Kokoszka [8], Section 3.2).”
Functional data analysis in the Banach space of continuous functions (2017, methodology): “The interested reader is referred to the various monographs Ferraty and Vieu [21], Horváth and Kokoszka [26], and Ramsay and Silverman [34] for up-to-date accounts.”
Functional Data Analysis (2016, background): “These problems pose novel challenges for data analysis (Horvath & Kokoszka 2012).”
Operator-valued Kernels for Learning from Functional Response Data (2015, background): “To this end, we extend the traditional scalar-valued attribute setting to a function-valued attribute setting.”
A survey of functional principal component analysis (2014, neutral): “( 2005b ) and Horváth & Kokoszka ( 2012 ) .”
Forecasting Functional Time Series with a New Hilbertian ARMAX Model: Application to Electricity Price Forecasting (2018, methodology): “It solves (1) using the equivalent Yule-Walker equations for Functional Data and estimating the operator Ψ as a function of the covariance operator of the time series (see [3] and [5] for more details).”