Results 251 to 260 of about 12,807,756 (320)
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On the estimation of the generalized covariance function
Journal of the International Association for Mathematical Geology, 1982T. H. Starks, J. Fang
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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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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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Testing for the significance of functional covariates
Journal of Multivariate Analysis, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Maistre, Samuel, Patilea, Valentin
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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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On Covariance Functions of Unit Processes
SIAM Journal on Applied Mathematics, 1972The problem of meaningful characterization of the covariance functions, “unit covariances,” of two-valued stationary stochastic processes is considered. Some general properties are derived. For some broad classes of unit covariances, corresponding to two-valued processes with a specified structure of axis crossing, complete and explicit ...
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On Some Characteristics of Gaussian Covariance Functions
International Statistical Review, 2020SummaryThe concepts of isotropy/anisotropy and separability/non‐separability of a covariance function are strictly related. If a covariance function is separable, it cannot be isotropic or geometrically anisotropic, except for the Gaussian covariance function, which is the only model both separable and isotropic. In this paper, some interesting results
De Iaco, Sandra +3 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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When is a truncated covariance function on the line a covariance function on the circle?
Statistics & Probability Letters, 1995zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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