4,035 research outputs found
Privacy sets for constrained space-filling
The paper provides typology for space filling into what we call "soft" and
"hard" methods along with introducing the central notion of privacy sets for
dealing with the latter. A heuristic algorithm based on this notion is
presented and we compare its performance on some well-known examples
Some Current Issues in the Statistical Analysis of Spillovers
Spillover phenomena are usually statistically estimated on the basis of regional and temporal panel data. In this paper we review and investigate exploratory and confirmatory statistical panel data techniques. We illustrate the methods by calculations in the stetting of the well known Research and Development Spillover study by Coe and Helpman (1995). It will be demonstrated that alternative estimation techniques that are well compatible with the data can lead to opposite conclusions.Panel data; fixed effects; random coefficients; DOLS; R&D spillover
Efficient Prediction Designs for Random Fields
For estimation and predictions of random fields it is increasingly
acknowledged that the kriging variance may be a poor representative of true
uncertainty. Experimental designs based on more elaborate criteria that are
appropriate for empirical kriging are then often non-space-filling and very
costly to determine. In this paper, we investigate the possibility of using a
compound criterion inspired by an equivalence theorem type relation to build
designs quasi-optimal for the empirical kriging variance, when space-filling
designs become unsuitable. Two algorithms are proposed, one relying on
stochastic optimization to explicitly identify the Pareto front, while the
second uses the surrogate criteria as local heuristic to chose the points at
which the (costly) true Empirical Kriging variance is effectively computed. We
illustrate the performance of the algorithms presented on both a simple
simulated example and a real oceanographic dataset
Optimal designs for regression models with a constant coefficient of variation
In this paper we consider the problem of constructing optimal designs for models with
a constant coefficient of variation. We explore the special structure of the information
matrix in these models and derive a characterization of optimal designs in the sense of
Kiefer and Wolfowitz (1960). Besides locally optimal designs, Bayesian and standardized
minimax optimal designs are also considered. Particular attention is spent on the problem
of constructing D-optimal designs. The results are illustrated in several examples where
optimal designs are calculated analytically and numerically
A criterion and incremental design construction for simultaneous kriging predictions
In this paper, we further investigate the problem of selecting a set of
design points for universal kriging, which is a widely used technique for
spatial data analysis. Our goal is to select the design points in order to make
simultaneous predictions of the random variable of interest at a finite number
of unsampled locations with maximum precision. Specifically, we consider as
response a correlated random field given by a linear model with an unknown
parameter vector and a spatial error correlation structure. We propose a new
design criterion that aims at simultaneously minimizing the variation of the
prediction errors at various points. We also present various efficient
techniques for incrementally buillding designs for that criterion scaling well
for high dimensions. Thus the method is particularly suitable for big data
applications in areas of spatial data analysis such as mining, hydrogeology,
natural resource monitoring, and environmental sciences or equivalently for any
computer simulation experiments. The effectiveness of the proposed designs is
demonstrated through numerical examples
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