Kriging regression with local error estimate
Kriging regression in digital image correlation for error reduction and uncertainty quantification
... 6, Kriging DIC considers the measurement uncertainty to be independent and identically distributed across the entire Region of Interest ...excellent error reduction properties, the resulting ...
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Uncertainty quantification in DIC with Kriging regression
... study Kriging regression is used as a post-processing technique to improve the accuracy of classical subset-based DIC measurement by including a local error estimate determined subset- ...
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Bootstrap Estimate of Prediction Error of Simple Linear Regression Models
... to estimate the variance of the sample median, and explained that it is an excellent alternative to the jackknife method which failed with this ...will estimate the prediction error using these two ...
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Bayesian Local Kriging
... try estimate covariance parameters from a few observations only, especially with a covariance structure not necessarily well-adapted to the variability of the modeled ...
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Local polynomial regression smoothers with AR-error structure
... We simulated B = 200 samples of size n from a fixed and equally spaced model in the interval [0, 1] with random errors following an AR(1) process with N (0, σ 2 ) distribution. Optimal bandwidths by minimizing the ...
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Ordinary kriging as a tool to estimate historical daily streamflow records
... ordinary kriging was extended to predict daily streamflow time series on a regional ...to estimate a single, time-invariant characteriza- tion of spatial semivariance structure is shown to produce accurate ...
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Object-Based Area-to-Point Regression Kriging for Pansharpening
... object-based regression part to exploit more accurate spatial detail of various objects from the PAN image for each sharpened FR MS ...the regression part, the spatial detail for some objects may not be ...
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Patchwork Kriging for Large-scale Gaussian Process Regression
... (GP) regression for large ...the regression input domain into multiple local regions with a different local GP model fitted in each ...existing local partitioned GP approaches, we ...
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On the Asymptotic Normality of an Estimate of a Regression Functional
... Let Y be a real valued random variable with E {Y 2 } < ∞ and let X = (X (1) , . . . , X (d) ) be a d-dimensional random observational vector. In regression analysis one wishes to estimate Y given X, ...
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Using Regression Kriging to Analyze Groundwater According to Depth and Capacity of Wells
... 2.5. Cross-Validation of RK Results To assess which spatial prediction method provides the most accurate interpolation method, cross-validation is used to compare the estimated values with their true values. ...
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Linear regression with Laplace measurement error
... bias-corrected estimate ˆ β BC has enjoyed its tremendous popularity in the errors-in-variables regression ...EV regression model with U , X, ε all from N (0, 1), U ∼ N (0, ...true regression ...
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Local modal regression
... A local modal estimation procedure is proposed for the regression function in a non- parametric regression ...ordinary local polynomial regression estimator in the presence of outliers ...
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A Fast Estimate for the Population Recombination Rate Based on Regression
... validation error is smallest is a successful strategy to prevent ...a regression model, the predictors X can be chosen to be summary statistics computed from the DNA ...
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Statistical methodology for regression model with measurement error
... 2.9 Maximum likelihood approach The application of the maximum likelihood method to measurement error models has been considered by many authors. The common approach to finding the maximum likelihood estimator is ...
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Quantile Regression for Repeated Responses Measured with Error.
... measurement error term following the classical measurement error assumptions (Carroll et ...the regression model aims to model the heteroscedas- ticity in the data, while the flexible distribution is ...
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Bayesian Estimation of Unknown Regression Error Heteroscedasticity
... reasonable estimate of ...to estimate a volatility process in time series data nonparametrically, what we usually do is to calculate a historical volatility ...
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Std. Error of the Estimate
... b. Predictors: (Constant), GROWTH, NDTS, Size, Dummy of DEF, STRUCTURE ANOVA a Further regression via confidence interval (CI) is done towards Extended Pecking Order model. As presented in Table 7, the result ...
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Consistency of the semivariogram fitting error on ordinary kriging prediction
... Kriging is a construction method that is primarily built based on the structure of experimental semivariograms and the power of fitting. The two functions, i.e., classical and robust semivariograms, are used in ...
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Kriging as an alternative to polynomial regression in response surface analysis
... investigated in this paper. The general equation defining the predictors is the same for both methods, with differences in the particular forms noted in Section 2.2.2. The ordinary krig[r] ...
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Using Kriging regression to improve the stability and diversity in NSGA-II
... the Kriging-assisted NSGA-II approach In this additional step we dont evaluate the fitness of the population with the simulator, we evaluate it with an emulator ...and Kriging- assisted ...
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