and Applied Analysis 3

We present a methodology to perform spatial prediction when measured data are curves. In particular, we propose both an estimator of the spatial correlation and a functional kriging predictor. We adapt an optimization criterium used in multivariable spatial prediction in order to estimate the kriging parameters. A real data example on soil penetration resistences illustrates our proposals.

Chaotic particle swarm optimization (CPSO) algorithm is proposed to optimize the Kriging model, which can improve the precision of curve fitting. A typical example is selected to demonstrate the advantage of the optimized Kriging model, compared with other curve fitting tools.

Nonlinear constrained optimization algorithms are widely utilized in artifact design. Certain algorithms also lend themselves well to design of experiments (DOE). Adaptive design refers to experimental design where determining where to sample next is influenced by information from previous experiments. We present a constrained optimization algorithm known as superEGO (a variant of the EGO algor...

The method of stochastic simulation is proposed to model the atmospheric effect on InSAR measurements based on sample data. Test results show that 37.44% reduction in the standard devisation of the atmospheric errors can be achieved with the method of stochastic simulation, compared to 25.69% with the method of Kriging interpolator. The relative improvement of the former over the latter amounts...

A simple algorithm for estimating the regression function over the United States is introduced. The approach allows for data obtained from a complicated sampling design, as well as for the inclusion of a few additional covariates. The regression estimates are obtained from an associated probability density estimate, namely the averaged shifted histogram. The algorithm has proven especially succ...

Ridge curves are important features in human vision (see Koenderink, 1990, p.295). In this paper we apply a simple algebraic criterion of Porteous (1994) to the problem of nding ridge curves in machine vision. This work provides a simpler approach than other existing methods (see for example, Thirion and Gourdon, 1992, and Hosaka, 1992). To identify ridge curves in practice on surface data it i...

Accurate mapping of total soil C on the field scale is essential for evaluating efforts to sequester soil C and for providing individual producers with information on C sequestration potentials of their fields. Data on easily measured secondary variables that are strongly related to soil C are believed to be helpful in improving mapping accuracy. The objective of this study was to assess improv...

In statistics it is often assumed that sample observations are independent. But sometimes in practice, observations are somehow dependent on each other. Spatiotemporal data are dependent data which their correlation is due to their spatiotemporal locations.Spatiotemporal models arise whenever data are collected across bothtime and space. Therefore such models have to be analyzed in termsof thei...