6,501 research outputs found
A complement to Le Cam's theorem
This paper examines asymptotic equivalence in the sense of Le Cam between
density estimation experiments and the accompanying Poisson experiments. The
significance of asymptotic equivalence is that all asymptotically optimal
statistical procedures can be carried over from one experiment to the other.
The equivalence given here is established under a weak assumption on the
parameter space . In particular, a sharp Besov smoothness
condition is given on which is sufficient for Poissonization,
namely, if is in a Besov ball with . Examples show Poissonization is not possible whenever .
In addition, asymptotic equivalence of the density estimation model and the
accompanying Poisson experiment is established for all compact subsets of
, a condition which includes all H\"{o}lder balls with smoothness
.Comment: Published at http://dx.doi.org/10.1214/009053607000000091 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Asymptotic equivalence and adaptive estimation for robust nonparametric regression
Asymptotic equivalence theory developed in the literature so far are only for
bounded loss functions. This limits the potential applications of the theory
because many commonly used loss functions in statistical inference are
unbounded. In this paper we develop asymptotic equivalence results for robust
nonparametric regression with unbounded loss functions. The results imply that
all the Gaussian nonparametric regression procedures can be robustified in a
unified way. A key step in our equivalence argument is to bin the data and then
take the median of each bin. The asymptotic equivalence results have
significant practical implications. To illustrate the general principles of the
equivalence argument we consider two important nonparametric inference
problems: robust estimation of the regression function and the estimation of a
quadratic functional. In both cases easily implementable procedures are
constructed and are shown to enjoy simultaneously a high degree of robustness
and adaptivity. Other problems such as construction of confidence sets and
nonparametric hypothesis testing can be handled in a similar fashion.Comment: Published in at http://dx.doi.org/10.1214/08-AOS681 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Sparse CCA: Adaptive Estimation and Computational Barriers
Canonical correlation analysis is a classical technique for exploring the
relationship between two sets of variables. It has important applications in
analyzing high dimensional datasets originated from genomics, imaging and other
fields. This paper considers adaptive minimax and computationally tractable
estimation of leading sparse canonical coefficient vectors in high dimensions.
First, we establish separate minimax estimation rates for canonical coefficient
vectors of each set of random variables under no structural assumption on
marginal covariance matrices. Second, we propose a computationally feasible
estimator to attain the optimal rates adaptively under an additional sample
size condition. Finally, we show that a sample size condition of this kind is
needed for any randomized polynomial-time estimator to be consistent, assuming
hardness of certain instances of the Planted Clique detection problem. The
result is faithful to the Gaussian models used in the paper. As a byproduct, we
obtain the first computational lower bounds for sparse PCA under the Gaussian
single spiked covariance model
Minimax estimation with thresholding and its application to wavelet analysis
Many statistical practices involve choosing between a full model and reduced
models where some coefficients are reduced to zero. Data were used to select a
model with estimated coefficients. Is it possible to do so and still come up
with an estimator always better than the traditional estimator based on the
full model? The James-Stein estimator is such an estimator, having a property
called minimaxity. However, the estimator considers only one reduced model,
namely the origin. Hence it reduces no coefficient estimator to zero or every
coefficient estimator to zero. In many applications including wavelet analysis,
what should be more desirable is to reduce to zero only the estimators smaller
than a threshold, called thresholding in this paper. Is it possible to
construct this kind of estimators which are minimax? In this paper, we
construct such minimax estimators which perform thresholding. We apply our
recommended estimator to the wavelet analysis and show that it performs the
best among the well-known estimators aiming simultaneously at estimation and
model selection. Some of our estimators are also shown to be asymptotically
optimal.Comment: Published at http://dx.doi.org/10.1214/009053604000000977 in the
Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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