5,870 research outputs found
Mixtures of Spatial Spline Regressions
We present an extension of the functional data analysis framework for
univariate functions to the analysis of surfaces: functions of two variables.
The spatial spline regression (SSR) approach developed can be used to model
surfaces that are sampled over a rectangular domain. Furthermore, combining SSR
with linear mixed effects models (LMM) allows for the analysis of populations
of surfaces, and combining the joint SSR-LMM method with finite mixture models
allows for the analysis of populations of surfaces with sub-family structures.
Through the mixtures of spatial splines regressions (MSSR) approach developed,
we present methodologies for clustering surfaces into sub-families, and for
performing surface-based discriminant analysis. The effectiveness of our
methodologies, as well as the modeling capabilities of the SSR model are
assessed through an application to handwritten character recognition
Depth-based classification for functional data
Classification is an important task when data are curves. Recently, the notion of statistical depth has been extended to deal with functional observations. In this paper, we propose robust procedures based on the concept of depth to classify curves. These techniques are applied to a real data example. An extensive simulation study with contaminated models illustrates the good robustness properties of these depth-based classification methods
SiFTO: An Empirical Method for Fitting SNe Ia Light Curves
We present SiFTO, a new empirical method for modeling type Ia supernovae (SNe
Ia) light curves by manipulating a spectral template. We make use of
high-redshift SN observations when training the model, allowing us to extend it
bluer than rest frame U. This increases the utility of our high-redshift SN
observations by allowing us to use more of the available data. We find that
when the shape of the light curve is described using a stretch prescription,
applying the same stretch at all wavelengths is not an adequate description.
SiFTO therefore uses a generalization of stretch which applies different
stretch factors as a function of both the wavelength of the observed filter and
the stretch in the rest-frame B band. We compare SiFTO to other published
light-curve models by applying them to the same set of SN photometry, and
demonstrate that SiFTO and SALT2 perform better than the alternatives when
judged by the scatter around the best fit luminosity distance relationship. We
further demonstrate that when SiFTO and SALT2 are trained on the same data set
the cosmological results agree.Comment: Modified to better match published version in Ap
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