An F test for linear models with functional responses

By Qing Shen, Julian J. Faraway. 2004

188 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/11460880fa16c32edc188ea461304bbc0825f31d

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Citations in the corpus

One‐Way anova for Functional Data via Globalizing the Pointwise F‐test (2014, methodology): “Via intensive simulation studies, we found that in terms of size controlling and power, the GPF test is comparable with the L2-norm-based test (Faraway, 1997; Zhang & Chen, 2007) and the F -type test (Shen & Faraway, 2004; Zhang, 2011) adopted for the one-way ANOVA problem for functional data.”
Adaptive Global Testing for Functional Linear Models (2014, background): “(Shen and Faraway 2004; Zhang and Chen 2007), nonparametric regression effects of scalar covariates on functional responeses (Cardot, Prchal, and Sarda 2007), and functional two-sample tests (Hall and van Keilegom 2007; Zhang and Chen 2007).”
A statistical method for estimating predictable differences between daily traffic flow profiles (2017, methodology): “The degrees of freedom adjustment factor can then be estimated (Shen and Faraway, 2004, p1246) by: After laying a fine grid over the functions, it is straightforward to compile E, the empirical covariance matrix based on the full model, using the covariances between flows at each time of the day…”
STATISTICAL INFERENCES FOR LINEAR MODELS WITH FUNCTIONAL RESPONSES (2011, methodology): “It may be a problem for those users who conduct the F -type test by looking at the F -table for proper critical values, but Shen and Faraway (2004) proposed truncating the approximate degrees of freedoms d1 and d2 to arrive at F[d1],[d2] where [a] denotes the closest integer to a.”
Second-Order Comparison of Gaussian Random Functions and the Geometry of DNA Minicircles (2010, methodology): “The literature on hypothesis testing for functional data is mostly concentrated on tests pertaining to the mean function (Fan and Lin 1998), as encountered, for instance, in functional linear models (Cardot et al. 2003; Cuevas, Febrero, and Fraiman 2004; Shen and Faraway 2004) or functional change d”
Pointwise testing with functional data using the Westfall–Young randomization method (2008, methodology): “There are methods to detect the overall difference in the mean curves (Fan and Lin, 1998; Shen and Faraway, 2004; and Zerbe and Murphy, 1986).”
Two-sample tests in functional data analysis starting from discrete data (2007, background): “Recent contributions to hypothesis testing in this field include those of Fan and Lin (1998), Locantore et al. (1999), Spitzner et al. (2003), Cuevas et al. (2004) and Shen and Faraway (2004).”