Results 1 to 10 of about 489,537 (279)
On the Covariance between Functions
Journal of Multivariate Analysis, 2002 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
exaly +2 more sources
Detecting independence of random vectors: generalized distance covariance and Gaussian covariance
Modern Stochastics: Theory and Applications, 2018 Distance covariance is a quantity to measure the dependence of two random vectors. We show that the original concept introduced and developed by Székely, Rizzo and Bakirov can be embedded into a more general framework based on symmetric Lévy measures and
Björn Böttcher +2 more
doaj +3 more sources
Multivariate Modeling of Some Datasets in Continuous Space and Discrete Time [PDF]
EntropyMultivariate space–time datasets are often collected at discrete, regularly monitored time intervals and are typically treated as components of time series in environmental science and other applied fields.
Rigele Te, Juan Du
doaj +2 more sources
FUNCTIONAL SEQUENTIAL TREATMENT ALLOCATION WITH COVARIATES [PDF]
Econometric Theory, 2023 We consider a sequential treatment problem with covariates. Given a realization of the covariate vector, instead of targeting the treatment with highest conditional expectation, the decision-maker targets the treatment which maximizes a general functional of the conditional potential outcome distribution, e.g., a conditional quantile, trimmed mean, or ...
Kock, Anders Bredahl +2 more
openaire +6 more sources
Functional PCA With Covariate-Dependent Mean and Covariance Structure
Technometrics, 2022 28 pages, 3 ...
Fei Ding +3 more
openaire +2 more sources
Separable spatio-temporal covariance functions
Lietuvos Matematikos Rinkinys, 2002 Ther is not abstract.
Kęstutis Dučinskas, Edita Lesauskienė
doaj +3 more sources
Learning a Depth Covariance Function
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 We propose learning a depth covariance function with applications to geometric vision tasks. Given RGB images as input, the covariance function can be flexibly used to define priors over depth functions, predictive distributions given observations, and methods for active point selection. We leverage these techniques for a selection of downstream tasks:
Eric Dexheimer, Andrew J. Davison
openaire +2 more sources
Estimation of genetic parameters for test-day milk yield in Khuzestan buffalo [PDF]
Pesquisa Agropecuária Brasileira, 2016 : The objective of this work was to estimate covariance functions for additive genetic and permanent environmental effects, as well as to obtain genetic parameters for buffalo test-day milk yield using random regression models on Legendre polynomials ...
Mostafa Madad +2 more
doaj +1 more source
Classification of Gaussian spatio-temporal data with stationary separable covariances
Nonlinear Analysis, 2021 The novel approach to classification of spatio-temporal data based on Bayes discriminant functions is developed. We focus on the problem of supervised classifying of the spatiotemporal Gaussian random field (GRF) observation into one of two classes ...
Marta Karaliutė, Kęstutis Dučinskas
doaj +1 more source
Means and covariance functions for geostatistical compositional data: an axiomatic approach [PDF]
, 2017 This work focuses on the characterization of the central tendency of a sample of compositional data. It provides new results about theoretical properties of means and covariance functions for compositional data, with an axiomatic perspective.
A Kolmogorov +29 more
core +3 more sources