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A NONPARAMETRIC ESTIMATOR FOR THE COVARIANCE FUNCTION OF FUNCTIONAL DATA
Econometric Theory, 2014Many quantities of interest in economics and finance can be represented as partially observed functional data. Examples include structural business cycle estimation, implied volatility smile, the yield curve. Having embedded these quantities into continuous random curves, estimation of the covariance function is needed to extract factors, perform ...
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Fuzzy function learning with covariance ellipsoids
IEEE International Conference on Neural Networks, 2002It is shown how first- and second-order statistics can estimate fuzzy rules and sets from input-output data. The fuzzy system approximates the function by covering its graph with fuzzy patches in the input-output state space. The neural quantizer system uses unsupervised competitive learning to estimate the local centroids and covariances of pattern ...
Julie A. Dickerson, Bart Kosko
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Modeling Frailty as a Function of Observed Covariates
Journal of Statistical Theory and Practice, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Govindarajulu, Usha S. +2 more
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The Characterization Problem for Isotropic Covariance Functions
Mathematical Geology, 1999zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Gneiting, Tilmann, Sasvári, Zoltán
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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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Generalized Covariance Functions and Their Applications in Estimation
manuscripta geodaetica, 1984zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Variogram and Covariance Function
1995The experimental variogram is a convenient tool for the analysis of spatial data as it is based on a simple measure of dissimilarity. Its theoretical counterpart reveals that a broad class of phenomena are adequately described by it, including phenomena of unbounded variation.
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Examples of Covariance Functions
1995We present a few models of covariance functions. They are defined for isotropic (i.e. rotation invariant) random functions. On the graphical representations the covariance functions are plotted as variograms using the relation γ(h) = C(0) - C(h).
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1995
It is actually difficult to characterize directly a covariance function matrix. This becomes easy in the spectral domain on the basis of Cramer’s generalization of the Bochner theorem, which is presented in this chapter. We consider complex covariance functions.
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It is actually difficult to characterize directly a covariance function matrix. This becomes easy in the spectral domain on the basis of Cramer’s generalization of the Bochner theorem, which is presented in this chapter. We consider complex covariance functions.
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ON THE CHOICE OF THE LOSS FUNCTION IN COVARIANCE ESTIMATION
Statistics & Risk Modeling, 1990Summary: In a generalized linear model, under certain conditions, the covariance matrices of a two-stage Aitken estimator and the Gauss-Markov estimator are related via Kariya's inequality of the form \[ Cov({\hat \beta}(\Omega))\leq Cov({\hat \beta}({\hat \Omega}))\leq \epsilon_{\gamma}[{\mathfrak L}(\gamma,{\hat \gamma})]Cov({\hat \beta}(\Omega)), \]
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