Determining the volume and mass of soil/dissolved contaminants is an important component in characterization and remediation design. The distribution and magnitude of detected concentrations are the basis from which the estimate of volume and mass are determined. Computer technologies have enhanced the capabilities of conducting the analysis by coupling three dimensional interpolation with visu...

Accuracy is increasingly recognized as an important dimension in geospatial information and analyses. A strategy well suited for map users who usually have limited information about map lineages is proposed for location-specific characterization of accuracy in land cover change maps. Logistic regression is used to predict the probabilities of correct change categorization based on local pattern...

In this work, the use of both a second-order response surface method (RSM) and the Kriging method as approximation models for design optimization are investigated and compared. After validating the accuracy of each method with simple one-and two-dimensional analytic functions, they are applied to two Supersonic Business Jet (SBJ) drag minimization design cases in order to obtain a clear compari...

The spatial pattern of soil moisture varies at different scales due to evapo-transpiration and precipitation which are transformed by topography, soil texture, and vegetation. The mapping of soil moisture by remote sensing has several advantages over conventional field measurement techniques especially in the case of heterogeneous landscapes. However, validation of low resolution satellite soil...

Abstract When influence coefficient (IC) method is used in rotor balancing, it is possible that serious errors could be resulted in unbalance determinations due to the contamination of measurements and IC matrix being ill-posed. In this study, the availability of least squares algorithm (LSA) and Tikhonov regularization (TR) is studied by error estimations of unbalance determination with consid...

Censored quantile regression has become an important alternative to the Cox proportional hazards model in survival analysis. In contrast to the central covariate effect from the meanbased hazard regression, quantile regression can effectively characterize the covariate effects at different quantiles of the survival time. When covariates are measured with errors, it is known that naively treatin...

We implement and assess a Markov chain Monte Carlo method with a surrogate forward computational model for determining the flight conditions of the HyShot II supersonic re-entry vehicle given pressure measurements. The surrogate models we examine are non-intrusive, i.e., sampling-based, and include (1) a dimension-reduced global polynomial surrogate, (2) ordinary Kriging, and (3) a low-rank sep...

In this paper we explore the consequences for forecasting of the following two facts: first, that over time statistical agencies revise and improve published data, so that observations on more recent events are those that are least well measured. Second, that economies are such that observations on the most recent events contain the the largest signal about the future. We discuss a variety of f...

Existing research on mixtures of regression models are limited to directly observed predictors. The estimation of mixtures of regression for measurement error data imposes challenges for statisticians. For linear regression models with measurement error data, the naive ordinary least squares method, which directly substitutes the observed surrogates for the unobserved error-prone variables, yie...

When modelling a large area, models that can take into a count the variation from the general mean in small sub-areas could perform better in prediction than a general model fitted to entire dataset. One method for adjusting the large-area models for such variation is kriging, in which the predictions are corrected with the aid of neighbouring observations. A variogram represents the spatial co...