Nonparametric Functional Data Analysis: Theory and Practice

By Mark C. Greenwood. 2007

138 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/9a6aebe35d3e4b41f3139d05b03ca1289d010887

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In Statistics and Probability

Citations in the corpus

Interval-wise testing for functional data (2017, methodology): “…that is the analysis of data sets characterized by the observations of one or more functions for each statistical unit, has recently achieved a considerable importance in modern statistical scientiﬁc investigation (Ramsay and Silverman 2002, 2005; Ferraty and Vieu 2006; Horv´ath and Kokoszka 2012).”
A survey of functional principal component analysis (2014, background): “( 2007 ) , Ferraty & Vieu ( 2006 ) , Mas ( 2002 ) , Mas ( 2008 ) , Bosq ( 2000 ) , among many others .”
Discrete triangular distributions and non-parametric estimation for probability mass function (2007, background): “About the secondary importance of continuous kernels, the readers can refer to [4, 5] for generality of (assumed) continuous data, [6] for multivariate cases, [7] for functional data, [8] for ordered categorial data, and [9] for discrete data.”
Penalized Functional Regression (2011, unknown): “…are functions (James 2002; Cardot, Ferraty, and Sarda 2003; Cardot and Sarda 2005; James and Silverman 2005; Müller 2005; Ramsay and Silverman 2005; Ferraty and Vieu 2006; Reiss and Ogden 2007; Jeff Goldsmith is Ph.D. Candidate (E-mail: jgoldsmi@jhsph.edu), Jennifer Bobb is Ph.D. Candidate…”
A Geometric Approach to Maximum Likelihood Estimation of the Functional Principal Components From Sparse Longitudinal Data (2007, background): “Ramsay and Silverman (2005) and Ferraty and Vieu (2006) gave an extensive survey of the applications of functional principal components analysis (FPCA).”
Rainbow Plots, Bagplots, and Boxplots for Functional Data (2010, background): “Ramsay & Silverman (2005) and Ferraty & Vieu (2006) provide detailed surveys of the many parametric and nonparametric techniques for analyzing functional data.”
An RKHS formulation of the inverse regression dimension-reduction problem (2009, methodology): “Consider a set of data recorded by the Tecator Infratec Food and Feed Analyzer, available at http://lib.stat.cmu.edu/datasets/tecator, and which were analyzed by Ferré and Yao (2005), Amato, Antoniadis and Feis (2006) and Ferraty and Vieu (2006).”