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PlugIn Estimators In Semiparametric Stochastic Process Models
"... Consider a locally asymptotically normal semiparametric model with a real parameter # and a possibly infinitedimensional parameter F . We are interested in estimating a realvalued functional a(F ). If ^ a # estimates a(F ) for known #, and ^ # estimates #, then the plugin estimator ^ a ^ # estima ..."
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Cited by 6 (5 self)
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Consider a locally asymptotically normal semiparametric model with a real parameter # and a possibly infinitedimensional parameter F . We are interested in estimating a realvalued functional a(F ). If ^ a # estimates a(F ) for known #, and ^ # estimates #, then the plugin estimator ^ a
Introduction Risk measures Plugin estimation of level sets Multivariate case Estimation of CTEα(X,Y) Perspectives and References
"... Plugin estimation of level sets in a noncompact setting with ..."
Optimal rates for plugin estimators of density level sets
"... In the context of density level set estimation, we study the convergence of general plugin methods under two main assumptions on the density for a given level λ. More precisely, it is assumed that the density (i) is smooth in a neighborhood of λ and (ii) has γexponent at level λ. Condition (i) ens ..."
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Cited by 13 (0 self)
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In the context of density level set estimation, we study the convergence of general plugin methods under two main assumptions on the density for a given level λ. More precisely, it is assumed that the density (i) is smooth in a neighborhood of λ and (ii) has γexponent at level λ. Condition (i
Total Error in a Plugin Estimator of Level Sets
, 2003
"... Given a probability density f on R^d, the minimum volume set of probability content á can be estimated by the level set of the same probability content corresponding to a kernel estimator of f. We obtain convergence rates for this plugin estimator with respect to a measurebased distance between se ..."
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Given a probability density f on R^d, the minimum volume set of probability content á can be estimated by the level set of the same probability content corresponding to a kernel estimator of f. We obtain convergence rates for this plugin estimator with respect to a measurebased distance between
Consistency of PlugIn Estimators of Upper Contour and Level Sets ∗
"... This note studies the problem of estimating the set of finite dimensional parameter values defined by a finite number of moment inequality or equality conditions and gives conditions under which the estimator defined by the set of parameter values that satisfy the estimated versions of these conditi ..."
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consists of inequality constraints on nonparametric regression functions and shows the consistency of the plugin estimator or Mestimators that agree with that estimator with probability approaching to one.
Optimal rates for plugin estimators of density level sets
, 2008
"... In the context of density level set estimation, we study the convergence of general plugin methods under two main assumptions on the density for a given level λ. More precisely, it is assumed that the density (i) is smooth in a neighborhood of λ and (ii) has γexponent at level λ. Condition (i) ens ..."
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In the context of density level set estimation, we study the convergence of general plugin methods under two main assumptions on the density for a given level λ. More precisely, it is assumed that the density (i) is smooth in a neighborhood of λ and (ii) has γexponent at level λ. Condition (i
A Combined AdaptiveMixtures/PlugIn Estimator of Multivariate Probability Densities
, 1996
"... : A multivariate extension of the plugin kernel (and filtered kernel) estimator is proposed which uses asymptotically optimal bandwidth matrix (matrices) for a normal mixture approximation of a density to be estimated (the filtered kernel estimator uses different matrices for different clusters of ..."
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Cited by 2 (1 self)
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: A multivariate extension of the plugin kernel (and filtered kernel) estimator is proposed which uses asymptotically optimal bandwidth matrix (matrices) for a normal mixture approximation of a density to be estimated (the filtered kernel estimator uses different matrices for different clusters
PLUGIN ESTIMATION OF LEVEL SETS IN A NONCOMPACT SETTING WITH APPLICATIONS IN MULTIVARIATE RISK THEORY
, 2011
"... This paper deals with the problem of estimating the level sets L(c) = {F(x) ≥ c}, with c ∈ (0,1), of an unknown distribution function F on R 2 +. A plugin approach is followed. That is, given a consistent estimator Fn of F, we estimate L(c) by Ln(c) = {Fn(x) ≥ c}. In our setting, noncompactnes ..."
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Cited by 8 (4 self)
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This paper deals with the problem of estimating the level sets L(c) = {F(x) ≥ c}, with c ∈ (0,1), of an unknown distribution function F on R 2 +. A plugin approach is followed. That is, given a consistent estimator Fn of F, we estimate L(c) by Ln(c) = {Fn(x) ≥ c}. In our setting
Results 1  10
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169,844