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Under very mild additional assumptions, translates of conditionally positive definite radial basis functions allow unique interpolation to scattered multivariate data, because the interpolation matrices have a symmetric and positive definite dominant part. In many applications, the data density varies locally according to the signal behaviour, and then the translates should get different scalings that match the local data density. Furthermore, if there is a local anisotropy in the data, the radial basis functions should possibly be distorted into functions with ellipsoids as level sets. In such cases, the symmetry and the definiteness of the matrices are no longer guaranteed. However, this brief note is the first paper to provide sufficient conditions for the unique solvability of such interpolation processes. The basic technique is a simple matrix perturbation argument combined with the Ball–Narcowich–Ward stability results. [ABSTRACT FROM AUTHOR]

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In this paper we study a method for the approximation by a monotone smooth function of a discrete set of observations. By this method it is possible to obtain a completely automatic program. We show that the solution is monotone with probability tending to one and by the test examples that it is sufficient in the practical case. [ABSTRACT FROM AUTHOR]

Copyright of Theoretical & Applied Climatology is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Calcolo is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The problem of the numerical approximation of multivariable functions has been solved by the Monte Carlo method when the data points are assumed to be given on discrete lattice points [5, 8, 2]. When the data points are randomly distributed and very numerous there are some results in the literature [3, 6] but if the number of the points is less than 2, where k is the dimension of the space, it is very difficult to develop approximation formulas. This paper gives a solution to this problem by local approximations. [ABSTRACT FROM AUTHOR]