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Home Research Topics Eye Movement Corpus Analysis Matuschek, Kliegl, Holschneider (2015, PLoS One). Smoothing Spline ANOVA Decomposition of Arbitrary Splines.

Matuschek, Kliegl, Holschneider (2015, PLoS One). Smoothing Spline ANOVA Decomposition of Arbitrary Splines.

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Matuschek, H., Kliegl, R., & Holschneider, M. (2015). Smoothing Spline ANOVA Decomposition of Arbitrary Splines: An Application to Eye Movements in Reading. PLoS One.

Abstract: The Smoothing Spline ANOVA (SS-ANOVA) requires a specialized construction of basis and penalty
terms in order to incorporate prior knowledge about the data to be fitted. Typically, one resorts to the
most general approach using tensor product splines. This implies severe constraints on the correlation
structure, i.e. the assumption of isotropy of smoothness can not be incorporated in general. This may
increase the variance of the spline fit, especially if only a relatively small set of observations are given.
In this article, we propose an alternative method that allows to incorporate prior knowledge without the
need to construct specialized bases and penalties, allowing the researcher to choose the spline basis and
penalty according to the prior knowledge of the observations rather than choosing them according to the
analysis to be done. The two approaches are compared with an artificial example and with analyses of
fixation durations during reading.

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Download this file (projectMarginal.R)projectMarginal.R[ ][ ]3 Kb03/02/2015 14:19
Download this file (fit_example_compare_full.R)fit_example_compare_full.R[ ][ ]9 Kb03/02/2015 14:19
Download this file (fit_example_compare.R)fit_example_compare.R[ ][ ]8 Kb03/02/2015 14:19
Download this file (DSdata.rda)DSdata.rda[ ][ ]3596 Kb03/02/2015 14:19
Download this file (PosthocDecompositionMatuschek2015.pdf)PosthocDecompositionMatuschek2015.pdf[ ][ ]1610 Kb03/02/2015 14:19
Last Updated on Saturday, 07 February 2015 16:19  

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