arXiv
(411,768 recursos)
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82.
Sparsity oracle inequalities for the Lasso - Bunea, Florentina; Tsybakov, Alexandre; Wegkamp, Marten
This paper studies oracle properties of $\ell_1$-penalized least squares in
nonparametric regression setting with random design.
83.
Deconvolution with unknown error distribution - Johannes, J.
We consider the problem of estimating a density $f_{X}$ using a sample
$Y_{1},...,Y_{n}$ from $f_{Y}=f_{X}*f_{\epsilon}$, where $f_{\epsilon}$ is an
unknown density function.
84.
Orthogonal arrays from Hermitian varieties - Aguglia, A.; Giuzzi, L.
An orthogonal array OA(q^{2n-1},q^{2n-2}, q,2) is constructed from the action
of a subset of PGL(n+1,q^2) on some non--degenerate Hermitian varieties in
PG(n,q^2).
86.
Efficient independent component analysis - Chen, Aiyou; Bickel, Peter J.
Independent component analysis (ICA) has been widely used for blind source
separation in many fields such as brain imaging analysis, signal processing and
telecommunication.
88.
Network tomography based on 1-D projections - Chen, Aiyou; Cao, Jin
Network tomography has been regarded as one of the most promising
methodologies for performance evaluation and diagnosis of the massive and
decentralized Internet.
94.
Adaptive Optimal Nonparametric Regression and Density Estimation Based
on Fourier-Legendre Expansion - Ostrovsky, E.; Zelikov, correspondent author; Y.
Motivated by finance and technical applications, the objective of this paper
is to consider adaptive estimation of regression and density distribution based
on Fourier-Legendre expansion, and construction of confidence intervals - also
adaptive.
95.
Additive Regression Model for Continuous Time Processes - Debbarh, Mohammed; Maillot, Bertrand
In the setting of additive regression model for continuous time process, we
establish the optimal uniform convergence rates and optimal asymptotic
quadratic error of additive regression.
98.
t-Wise Independence with Local Dependencies - Gradwohl, Ronen; Yehudayoff, Amir
In this note we prove a large deviation bound on the sum of random variables
with the following dependency structure: there is a dependency graph $G$ with a
bounded chromatic number, in which each vertex represents a random variable.
81.
