Second-Order Comparison of Gaussian Random Functions and the Geometry of DNA Minicircles

By Victor M. Panaretos, David Kraus, John H. Maddocks. 2010

112 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/eb1ae6694267fda0656f5673abe8313192c552e3

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In Molecular Biology

Citations in the corpus

Testing the Equality of Covariance Operators in Functional Samples (2011, background): “Panaretos et al. (2010) proposed a test of the type we consider, but assuming that the curves have a Gaussian distribution.”
Dispersion operators and resistant second-order functional data analysis (2012, methodology): “Using inverse weights induced by Gervini’s (2008) spatial median, Panaretos et al. (2010) identified five unusual curves, possible outliers, and removed them from the analysis of the covariance structure.”
Selected statistical methods of data analysis for multivariate functional data (2018, background): “Berrenderoa et al. (2011), Jacques and Preda (2014) and Panaretos et al. (2010) discussed principal component analysis for multivariate functional data (MFPCA).”
Distances and inference for covariance operators (2014, methodology): “Some recent work (Panaretos et al., 2010; Fremdt et al., 2013) has examined testing the equality of covariance structures from two groups of functional curves by defining a test statistic through the Karhunen–Loève expansions of the two covariance structures.”
Bootstrap-based testing of equality of mean functions or equality of covariance operators for functional data (2016, methodology): “This has motivated many researchers to consider test statistics that are based on finite-dimensional projections, leading to more tractable asymptotic distributions; see Horváth & Kokoszka (2012, Ch. 5), Panaretos et al. (2010) and Fremdt et al. (2012).”
Testing relevant hypotheses in functional time series via self‐normalization (2018, methodology): “It is worthwhile to mention that the distribution of W is not the same as the one in previous work on self-normalization [see for example Shao (2010) or Shao (2015)] and quantiles of this distribution need to be simulated first.”
Functional Data Analysis (2016, methodology): “Other two sample tests have been proposed for distributions of functional data (Hall & Van Keilegom 2007) and for covariance functions (Panaretos, Kraus & Maddocks 2010; Boente, Rodriguez & Sued 2011).”