135 research outputs found
Adaptive density deconvolution with dependent inputs
In the convolution model , we give a model selection
procedure to estimate the density of the unobserved variables , when the sequence is strictly stationary but
not necessarily independent. This procedure depends on wether the density of
is super smooth or ordinary smooth. The rates of convergence of
the penalized contrast estimators are the same as in the independent framework,
and are minimax over most classes of regularity on . Our results
apply to mixing sequences, but also to many other dependent sequences. When the
errors are super smooth, the condition on the dependence coefficients is the
minimal condition of that type ensuring that the sequence
is not a long-memory process
Penalized contrast estimator for adaptive density deconvolution
The authors consider the problem of estimating the density of independent
and identically distributed variables , from a sample
where , , is a noise
independent of , with having known distribution. They
present a model selection procedure allowing to construct an adaptive estimator
of and to find non-asymptotic bounds for its
-risk. The estimator achieves the minimax rate of
convergence, in most cases where lowers bounds are available. A simulation
study gives an illustration of the good practical performances of the method
Nonparametric Econometric Methods and Application
The present Special Issue collects a number of new contributions both at the theoretical level and in terms of applications in the areas of nonparametric and semiparametric econometric methods. In particular, this collection of papers that cover areas such as developments in local smoothing techniques, splines, series estimators, and wavelets will add to the existing rich literature on these subjects and enhance our ability to use data to test economic hypotheses in a variety of fields, such as financial economics, microeconomics, macroeconomics, labor economics, and economic growth, to name a few
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