We present a novel approach for the inverse problem in electrical impedance tomography based on regularized quadratic regression. Our contribution introduces a new formulation for the forward model in the form of a nonlinear integral transform, that maps changes in the electrical properties of a domain to their respective variations in boundary data. Using perturbation theory results the kernel...

Regression problems on massive data sets are ubiquitous in many application domains including the Internet, earth and space sciences, and finances. Gaussian Process regression is a popular technique for modeling the input-output relations of a set of variables under the assumption that the weight vector has a Gaussian prior. However, it is challenging to apply Gaussian Process regression to lar...

We consider the smoothed version of sliced average variance estimation (SAVE) dimension reduction method for dealing with spatially dependent data that are observations a strongly mixing random field. propose kernel estimators interest matrix and effective (EDR) space, show their consistency.

Abstract : It has been recently shown that nonparametric estimates of the additive regression function could be obtained in presence of censored data by coupling the marginal integration method with initial kernel-type Inverse Probability of Censoring Weighted estimators of the multivariate regression function (7). In this paper, we get the exact rate of strong uniform consistency for such esti...

The multi-class metric problem in nearest neighbour dis-21 nearest neighbors than we achieved. Friedman (1994) proposes a number of techniques for exible metric nearest neighbor classiication. These techniques use a recursive partitioning style strategy to adaptively shrink and shape rectangular neighborhoods around the test point. Friedman also uses derived variables in the process, including ...

With advancing of modern technologies, high-dimensional data have prevailed in computational biology. The number of variables p is very large, and in many applications, p is larger than the number of observational units n. Such high dimensionality and the unconventional small-n-large-p setting have posed new challenges to statistical analysis methods. Dimension reduction, which aims to reduce t...