We address the problem of one dimensional segment detection and estimation, in a regression setup. At each point of a fixed or random design, one observes whether that point belongs to the unknown seg-ment or not, up to some additional noise. We try to understand what the minimal size of the segment is so it can be accurately seen by some statistical procedure, and how this minimal size depends on some a priori knowledge about the location of the unknown segment
Bibliography: p. 56-58.Supported by the National Science Foundation grant ECS-8312921 Supported by t...
Modern science and engineering often generate data sets with a large sample size and a comparably la...
State estimation entails the estimation of an unobserved random closed set from (partial) observatio...
We address the problem of one dimensional segment detection and estimation, in a regression setup. A...
We address the problem of detection and estimation of one or two change-points in the mean of a seri...
In this thesis, we are interested in statistical inference on convex bodies in the Euclidean space R...
In this thesis, we are interested in statistical inference on convex bodies in the Euclidean space $...
summary:A method of geometrical characterization of multidimensional data sets, including constructi...
In robotic vision using laser-radar measurements, noisy data on convex sets with corners are derived...
A set in the Euclidean plane is said to be biconvex if, for some angle θ ∈ [0, π∕2), all its section...
We present a minimax optimal solution to the problem of estimating a compact, convex set from finite...
International audienceThis work is closely related to the theories of set estimation and manifold es...
AbstractState estimation entails the estimation of an unobserved random closed set from (partial) ob...
International audienceWe discuss a general approach to handling a class of nonparametric detection p...
When sampling minimal subsets for robust parameter estimation, it is commonly known that obtaining a...
Bibliography: p. 56-58.Supported by the National Science Foundation grant ECS-8312921 Supported by t...
Modern science and engineering often generate data sets with a large sample size and a comparably la...
State estimation entails the estimation of an unobserved random closed set from (partial) observatio...
We address the problem of one dimensional segment detection and estimation, in a regression setup. A...
We address the problem of detection and estimation of one or two change-points in the mean of a seri...
In this thesis, we are interested in statistical inference on convex bodies in the Euclidean space R...
In this thesis, we are interested in statistical inference on convex bodies in the Euclidean space $...
summary:A method of geometrical characterization of multidimensional data sets, including constructi...
In robotic vision using laser-radar measurements, noisy data on convex sets with corners are derived...
A set in the Euclidean plane is said to be biconvex if, for some angle θ ∈ [0, π∕2), all its section...
We present a minimax optimal solution to the problem of estimating a compact, convex set from finite...
International audienceThis work is closely related to the theories of set estimation and manifold es...
AbstractState estimation entails the estimation of an unobserved random closed set from (partial) ob...
International audienceWe discuss a general approach to handling a class of nonparametric detection p...
When sampling minimal subsets for robust parameter estimation, it is commonly known that obtaining a...
Bibliography: p. 56-58.Supported by the National Science Foundation grant ECS-8312921 Supported by t...
Modern science and engineering often generate data sets with a large sample size and a comparably la...
State estimation entails the estimation of an unobserved random closed set from (partial) observatio...