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ASYMPTOTIC EQUIVALENCE OF DENSITY ESTIMATION AND GAUSSIAN WHITE NOISE
ASYMPTOTIC EQUIVALENCE DENSITY ESTIMATION GAUSSIAN WHITE NOISE
2015/8/25
Signal recovery in Gaussian white noise with variance tending to zero has served for some time as a representative model for nonparametric curve estimation, having all the essential traits in a pure f...
ON THE ESTIMATION OF A SUPPORT CURVE OF INDETERMINATE SHARPNESS
Convergence rate curve estimation endpoint order statistic regular variation support
2015/8/25
We propose nonparametric methods for estimating the support curve of a bivariate density, when the density decreases at a rate which might vary along the curve. Attention is focussed on cases where th...
Asymptotic Equivalence of Density Estimation and Gaussian White Noise
Asymptotic Equivalence Density Estimation Gaussian White Noise
2015/8/25
Signal recovery in Gaussian white noise with variance tending to zero has served for some time as a representative model for nonparametric curve estimation, having all the essential traits in a pure f...
An Information-Theoretic Approach to Traffic Matrix Estimation
Traffi c Matrix Estimation Information Theory
2015/8/21
Traffic matrices are required inputs for many IP network management tasks: for instance, capacity planning, traffic engineering and
network reliability analysis. However, it is diffi...
DISCUSSION:THE DANTZIG SELECTOR:STATISTICAL ESTIMATION WHEN p IS MUCH LARGER THAN n
DANTZIG SELECTOR STATISTICAL ESTIMATION p IS MUCH LARGER THAN n
2015/8/21
This is a fascinating paper on an important topic: the choice of predictor variables in large-scale linear models. A previous paper in these pages attacked the same problem using the “LARS” algorithm ...
Sparse inverse covariance estimation with the lasso
Sparse inverse covariance estimation the lasso
2015/8/21
We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm— the ...
Applications of the lasso and grouped lasso to the estimation of sparse graphical models
lasso and grouped lasso sparse graphical models
2015/8/21
We propose several methods for estimating edge-sparse and nodesparse graphical models based on lasso and grouped lasso penalties.We develop efficient algorithms for fitting these models when the numbe...
On minimax estimation of a sparse normal mean vector
nearly black object robustness white noise model
2015/8/20
Mallows has conjectured that among distributions which are Gaussian but
for occasional contamination by additive noise, the one having least Fisher
information has (two-sided) geometric contaminatio...
Wavelets and the theory of non-parametric function estimation
Pinsker’s theorem sparsity statistical decision problem
2015/8/20
Non-parametric function estimation aims to estimate or recover or denoise a function
of interest, perhaps a signal, spectrum or image, that is observed in noise and possibly
indirectly after some tr...
We attempt to recover an unknown function from noisy, sampled data. Using
orthonormal bases of compactly supported wavelets we develop a nonlinear method
which works in the wavelet domain by simple ...
Density estimation is a commonly used test case for non-parametric estimation
methods. We explore the asymptotic properties of estimators based on thresholding of
empirical wavelet coecients. Minim...
Selection and Estimation for Large-Scale Simultaneous Inference
Large-Scale Simultaneous Inference Selection
2015/8/20
Modern scientific technology is providing a new class of simultaneous inference
problems for the applied statistician, where there are hundreds or thousands or even
more hypothesis tests to co...
Two modeling strategies for empirical Bayes estimation
f-modeling g-modeling Bayes rule in terms of f
2015/8/20
Empirical Bayes methods use the data from parallel experiments, for instance observtions Xk N (k;1) for k = 1;2; : : : ; N, to estimate the conditional distributions kjXk.
There are two main estima...
Estimation and Accuracy after Model Selection
model averaging Cp, Lasso bagging bootstrap smoothing
2015/8/20
Classical statistical theory ignores model selection in assessing estimation accuracy. Here we consider
bootstrap methods for computing standard errors and condence intervals that take model selecti...
Classical statistical theory ignores model selection in assessing estimation accuracy. Here we consider
bootstrap methods for computing standard errors and condence intervals that take model selecti...