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A note on extreme values and kernel estimators of sample boundaries
support estimation asymptotic normality kernel estimator ex-treme values.
2012/9/18
In a previous paper [3], we studied a kernel estimate of the upper edge of a two-dimensional bounded set, based upon the extreme values of a Poisson point process. The initial paper [1] on the subject...
Using prior information to identify boundaries in disease risk maps
Conditional Autoregressive priors Disease mapping Lo-calised spatial smoothing
2012/9/17
Disease maps display the spatial pattern in disease risk, so that high-risk clusters can be identi ed. The spatial structure in the risk map is typically represented by a set of random e ects, which a...
Sparse classification boundaries
Bayes risk classification boundary high-dimensional data optimalclassifier sparse vectors
2010/3/19
Given a training sample of size m from a d-dimensional population, we
wish to allocate a new observation Z ∈ IRd to this population or to the noise.
We suppose that the difference between the distri...
Renewal series and square-root boundaries for Bessel processes
Renewal series square-root boundaries Bessel processes
2009/3/23
We show how a description of Brownian exponential functionals as a renewal series gives access to the law of the hitting time of a square-root boundary by a Bessel process. This extends classical resu...
Expectations of Random Sets and Their Boundaries Using Oriented Distance Functions
Random Sets Boundaries Oriented Distance Functions
2010/3/18
Shape estimation and object reconstruction are common problems in image analysis. Mathematically,viewing objects in the image plane as random sets reduces the problem of shape estimation to inference ...