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Sparse estimation in kriging for functional data
Stochastic Environmental Research and Risk Assessment, 2023 We introduce a sparse estimation in the ordinary kriging for functional data. The functional kriging predicts a feature given as a function at a location where the data are not observed by a linear combination of data observed at other locations. To estimate the weights of the linear combination, we apply the lasso-type regularization in minimizing the
Yuya Yamakawa +2 more
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Prediction of spatial functional random processes: comparing functional and spatio-temporal kriging approaches [PDF]
Stochastic Environmental Research and Risk Assessment, 2019 33 pages, 11 ...
Jorge Mateu +2 more
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A universal kriging approach for spatial functional data
Stochastic Environmental Research and Risk Assessment, 2013In a wide range of scientific fields the outputs coming from certain measurements often come in form of curves. In this paper we give a solution to the problem of spatial prediction of non-stationary functional data. We propose a new predictor by extending the classical universal kriging predictor for univariate data to the context of functional data ...
Jorge Mateu +2 more
exaly +3 more sources
Kriging-based optimization of functionally graded structures
Structural and Multidisciplinary Optimization, 2021This work presents an efficient methodology for the optimum design of functionally graded structures using a Kriging-based approach. The method combines an adaptive Kriging framework with a hybrid particle swarm optimization (PSO) algorithm to improve the computational efficiency of the optimization process.
Marina Alves Maia +2 more
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A novel learning function based on Kriging for reliability analysis
Reliability Engineering & System Safety, 2020Abstract Adaptively constructing the surrogate model for reliability analysis has been widely studied for the advantage of guaranteeing the estimation accuracy while calling the real performance function as little as possible. A new learning function called Folded Normal based Expected Improvement Function (FNEIF) is proposed to efficiently estimate ...
Yan Shi 0014 +4 more
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Conditional optimization of a noisy function using a kriging metamodel
Journal of Global Optimization, 2019The efficient global optimization method is popular for the global optimization of computer-intensive black-box functions. Extensions exist, either for the optimization of noisy functions, or for the conditional optimization of deterministic functions, i.e.
Diariétou Sambakhé +3 more
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Kriging with Nonparametric Variance Function Estimation
1998A method for fitting regression models to data that exhibit spatial correlation and Heteroskedasticity is proposed. A combination of parametric and nonparametric regression techniques is used to iteratively estimate the various components of the model. The approach is demonstrated on a large dataset of predicted nitrogen runoff from agricultural lands ...
Opsomer, Jean D. +9 more
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The equivalence of predictions from universal kriging and intrinsic random-function kriging
Mathematical Geology, 1990A proof is provided that the predictions obtained from kriging based on intrinsic random functions of orderk are identical to those obtained from anappropriate universal kriging model. This is a theoretical result based on known variability measures. It does not imply that people performing traditional universal kriging will get the same predictions as
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Conformal Prediction for Functional Kriging Models
2023In this work we introduce a conformal prediction method for functional kriging. Conformal Prediction (CP) is a framework in machine learning and statistical inference that provides a principled way to quantify uncertainty and make predictions without relying on specific distributional assumptions.
Diana A., Romano E., Adzic J.
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Ordinary kriging for function-valued spatial data
Environmental and Ecological Statistics, 2010In various scientific fields properties are represented by functions varying over space. In this paper, we present a methodology to make spatial predictions at non-data locations when the data values are functions. In particular, we propose both an estimator of the spatial correlation and a functional kriging predictor.
Giraldo, R. +2 more
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