Results 231 to 240 of about 716,617 (262)
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Functional Data Analysis with Covariate-Dependent Mean and Covariance Structures
Biometrics, 2022Abstract Functional data analysis has emerged as a powerful tool in response to the ever-increasing resources and efforts devoted to collecting information about response curves or anything that varies over a continuum. However, limited progress has been made with regard to linking the covariance structures of response curves to external
Chenlin Zhang +4 more
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Bulletin Géodésique, 1987
Because of the full covariance matrices and the computer storage limitations the number of measurements which can be handled by the collocation method simultaneously, is limited. This paper presents a method to compute covariance functions with a finite support yielding sparse covariance matrices.
F. Sansò, W. -D. Schuh
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Because of the full covariance matrices and the computer storage limitations the number of measurements which can be handled by the collocation method simultaneously, is limited. This paper presents a method to compute covariance functions with a finite support yielding sparse covariance matrices.
F. Sansò, W. -D. Schuh
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Modern Physics Letters A, 1993
The differential realization of the recently proposed deformed Poincaré algebra is considered. The notion of covariant wave functions is introduced and their explicit form in the "minimal" (in Weinberg's sense) case is given. The deformed Dirac equation is constructed.
S. GILLER +5 more
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The differential realization of the recently proposed deformed Poincaré algebra is considered. The notion of covariant wave functions is introduced and their explicit form in the "minimal" (in Weinberg's sense) case is given. The deformed Dirac equation is constructed.
S. GILLER +5 more
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Random forests for functional covariates
Journal of Chemometrics, 2016We propose a form of random forests that is especially suited for functional covariates. The method is based on partitioning the functions' domain in intervals and using the functions' mean values across those intervals as predictors in regression or classification trees.
Möller, Annette Christine +2 more
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Estimating the covariance function with functional data
British Journal of Mathematical and Statistical Psychology, 2002This paper describes a two‐step procedure for estimating the covariance function and its eigenvalues and eigenfunctions in situations where the data are curves or functions. The first step produces initial estimates of eigenfunctions using a standard principal components analysis.
Sik-Yum, Lee +2 more
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Flexible spatial covariance functions
Spatial Statistics, 2020Abstract We focus on the discussion of modeling processes that are observed at fixed locations of a region (geostatistics). A standard approach is to assume that the process of interest follows a Gaussian Process with some mean and (valid) covariance functions. It is common to model the covariance function as the product between a variance parameter,
Alexandra M. Schmidt, Peter Guttorp
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2015
We often assume that Gaussian processes are isotropic implying that the covariance function only depends on the distance between locations.
Yunfei Xu +3 more
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We often assume that Gaussian processes are isotropic implying that the covariance function only depends on the distance between locations.
Yunfei Xu +3 more
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