covariance function
Optimal Nonparametric Covariance Function Estimation for Any Family of Nonstationary Random Processes
... kernel function is computed as described in Theorem 1 and can be seen in Figure ...parametric covariance function estimators are not guaran- teed to be non-negative definite, ...resulting ...
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A covariance function for feed intake, live weight and milk yield estimated using a random regression model
... multitrait covariance func- tions were used to describe genetic covariances among feed intake, live weight, and milk yield during the first 15 wk of lactation ( n = ...estimate covariance functions for the ...
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PubMedCentral-PMC5710774.pdf
... Motivated by the analysis of imaging data, we propose a novel functional varying-coefficient single index model (FVCSIM) to carry out the regression analysis of functional response data on a set of covariates of ...
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QUANTITATIVE ANALYSIS OF BANDED STRUCTURES IN DUAL-PHASE STEELS
... Dual-Phase (DP) steels are composed of martensite islands dispersed in a ductile ferrite matrix, which provides a good balance between strength and ductility. Current processing conditions (continuous casting followed by ...
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A comparison of alternative approaches to supremum norm goodness of fit tests with estimated parameters
... the covariance function of the parametric empirical process is smaller than that of the Brownian bridge for all t , and therefore critical values for the Kolmogorov-Smirnov test using the parametric ...
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Inhibitory geostatistical designs for spatial prediction taking account of uncertain covariance structure
... to cover D. A combination of theoretical and empirical work, from Mat´ ern (1960) onwards, has led to general acceptance that lattice designs should lead to efficient spatial prediction provided model parameters are ...
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Online Full Text
... Abstract—A unified technique for generating lin- ear operations on homogeneous/non-homogeneous, Gaussian/non-Gaussian random fields defined on any subset of the multidimensional Euclidean space is pro- vided. This is ...
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Variability of flow discharge in lateral inflow dominated stream channels
... The problem of fluctuations in flow discharge in open chan- nels in response to temporal changes in lateral inflow rate is investigated stochastically for a finite flow domain. In this study, the inflow perturbation ...
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Digital Communication Receivers Using Gaussian Processes for Machine Learning
... To highlight the advantages of GPs as digital com- munications receivers we compare their performances to that of SVM. SVM provides solutions as good as the GPR does, but it needs more training samples. The GPR fits its ...
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Multi-quadric collocation model of horizontal crustal movement
... signal covariance functions are generally built by adopting observed data based on certain principles (Zeng et ...priori covariance matrix determined in this way has a strong correlation with the current ...
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Estimation of the mixing kernel and the disturbance covariance in IDE-based spatiotemporal systems
... The integro-difference equation (IDE) is an increasingly popular mathematical model of spatiotemporal processes, such as brain dynamics, weather systems, disease spread and others. We present an efficient approach for ...
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Analysis of the inheritance, selection and evolution of growth trajectories.
... The additive genetic covariance function 59 plays the same role in the evolution of growth trajectories that the additive genetic covariance matrix does in the stan[r] ...
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Estimation of diffusion coefficients from voltammetric signals by support vector and gaussian process regression
... exponential covariance function is higher as compared to the Nicholson-Shain equation approach over a wide range of rate ...exponential covariance function is better suited than SVR to ...
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Considering groups in the statistical modeling of spatio-temporal data
... spatio-temporal covariance function cannot be obtained as the sum or the prod- uct of a spatial covariance function and a temporal covariance ...space-time covariance ...
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MODELS OF COVARIANCE FUNCTIONS OF GAUSSIAN RANDOM FIELDS ESCAPING FROM ISOTROPY, STATIONARITY AND NON NEGATIVITY
... 2013): covariance among two locations depends solely on their Euclidean ...the covariance function can be written as a function of one non negative number, and all couples of locations showing ...
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Shape error modelling and analysis by conditional simulations of Gaussian random fields for compliant non ideal sheet metal parts
... the covariance function char- acterising the variation of a given part, obtained as described in Section ...the covariance pa- rameters were optimised, leading to realistic non-ideal part rep- ...
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A new class of nonseparable space time covariance models
... correlation function versus the empirical cor- relation is presented in Figure 4 for the separable and nonseparable models (for all 55 pairs of ...the covariance function which is not adequate for ...
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Lag time determination in DEC measurements with PTR-MS
... correlation coefficient varied between 0.57 and 0.79. The differences were not significant at the 95% level, so the cor- relation analysis did not yield a conclusive result. Like in previous studies (Rinne et al., 2007; ...
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A first sketch: Construction of model defect priors inspired by dynamic time warping
... structured covariance func- tion by a very flexible one and to infer its shape by taking into account reaction systems that can be considered similar, ...tial covariance function are replaced by an ...
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Analysis error covariance versus posterior covariance in variational data assimilation
... error covariance matrix (see, for example, Thacker, 1989; Rabier and Courtier, 1992; Fisher and Courtier, 1995; Yang et ...posterior covariance is not exactly the analysis error ...error covariance ...
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