148 research outputs found
Adaptive Bayesian estimation in indirect Gaussian sequence space models
In an indirect Gaussian sequence space model lower and upper bounds are
derived for the concentration rate of the posterior distribution of the
parameter of interest shrinking to the parameter value that
generates the data. While this establishes posterior consistency, however, the
concentration rate depends on both and a tuning parameter which
enters the prior distribution. We first provide an oracle optimal choice of the
tuning parameter, i.e., optimized for each separately. The
optimal choice of the prior distribution allows us to derive an oracle optimal
concentration rate of the associated posterior distribution. Moreover, for a
given class of parameters and a suitable choice of the tuning parameter, we
show that the resulting uniform concentration rate over the given class is
optimal in a minimax sense. Finally, we construct a hierarchical prior that is
adaptive. This means that, given a parameter or a class of
parameters, respectively, the posterior distribution contracts at the oracle
rate or at the minimax rate over the class. Notably, the hierarchical prior
does not depend neither on nor on the given class. Moreover,
convergence of the fully data-driven Bayes estimator at the oracle or at the
minimax rate is established
Adaptive estimation of linear functionals in functional linear models
We consider the estimation of the value of a linear functional of the slope
parameter in functional linear regression, where scalar responses are modeled
in dependence of random functions. In Johannes and Schenk [2010] it has been
shown that a plug-in estimator based on dimension reduction and additional
thresholding can attain minimax optimal rates of convergence up to a constant.
However, this estimation procedure requires an optimal choice of a tuning
parameter with regard to certain characteristics of the slope function and the
covariance operator associated with the functional regressor. As these are
unknown in practice, we investigate a fully data-driven choice of the tuning
parameter based on a combination of model selection and Lepski's method, which
is inspired by the recent work of Goldenshluger and Lepski [2011]. The tuning
parameter is selected as the minimizer of a stochastic penalized contrast
function imitating Lepski's method among a random collection of admissible
values. We show that this adaptive procedure attains the lower bound for the
minimax risk up to a logarithmic factor over a wide range of classes of slope
functions and covariance operators. In particular, our theory covers point-wise
estimation as well as the estimation of local averages of the slope parameter
On large-scale diagonalization techniques for the Anderson model of localization
We propose efficient preconditioning algorithms for an eigenvalue problem arising in quantum physics, namely the computation of a few interior eigenvalues and their associated eigenvectors for large-scale sparse real and symmetric indefinite matrices of the Anderson model
of localization. We compare the Lanczos algorithm in the 1987 implementation by Cullum and Willoughby with the shift-and-invert techniques in the implicitly restarted Lanczos method and in the Jacobi–Davidson method. Our preconditioning approaches for the shift-and-invert symmetric indefinite linear system are based on maximum weighted matchings and algebraic multilevel incomplete
LDLT factorizations. These techniques can be seen as a complement to the alternative idea of using more complete pivoting techniques for the highly ill-conditioned symmetric indefinite Anderson matrices. We demonstrate the effectiveness and the numerical accuracy of these algorithms. Our numerical examples reveal that recent algebraic multilevel preconditioning solvers can accelerate the computation of a large-scale eigenvalue problem corresponding to the Anderson model of localization
by several orders of magnitude
A steerable UV laser system for the calibration of liquid argon time projection chambers
A number of liquid argon time projection chambers (LAr TPC's) are being build
or are proposed for neutrino experiments on long- and short baseline beams. For
these detectors a distortion in the drift field due to geometrical or physics
reasons can affect the reconstruction of the events. Depending on the TPC
geometry and electric drift field intensity this distortion could be of the
same magnitude as the drift field itself. Recently, we presented a method to
calibrate the drift field and correct for these possible distortions. While
straight cosmic ray muon tracks could be used for calibration, multiple coulomb
scattering and momentum uncertainties allow only a limited resolution. A UV
laser instead can create straight ionization tracks in liquid argon, and allows
one to map the drift field along different paths in the TPC inner volume. Here
we present a UV laser feed-through design with a steerable UV mirror immersed
in liquid argon that can point the laser beam at many locations through the
TPC. The straight ionization paths are sensitive to drift field distortions, a
fit of these distortion to the linear optical path allows to extract the drift
field, by using these laser tracks along the whole TPC volume one can obtain a
3D drift field map. The UV laser feed-through assembly is a prototype of the
system that will be used for the MicroBooNE experiment at the Fermi National
Accelerator Laboratory (FNAL)
Measurement of the drift field in the ARGONTUBE LAr TPC with 266~nm pulsed laser beams
ARGONTUBE is a liquid argon time projection chamber (LAr TPC) with a drift
field generated in-situ by a Greinacher voltage multiplier circuit. We present
results on the measurement of the drift-field distribution inside ARGONTUBE
using straight ionization tracks generated by an intense UV laser beam. Our
analysis is based on a simplified model of the charging of a multi-stage
Greinacher circuit to describe the voltages on the field cage rings
Experimental study of electric breakdowns in liquid argon at centimeter scale
In this paper we present results on measurements of the dielectric strength
of liquid argon near its boiling point and cathode-anode distances in the range
of 0.1 mm to 40 mm with spherical cathode and plane anode. We show that at such
distances the applied electric field at which breakdowns occur is as low as 40
kV/cm. Flash-overs across the ribbed dielectric of the high voltage
feed-through are observed for a length of 300 mm starting from a voltage of 55
kV. These results contribute to set reference for the breakdown-free design of
ionization detectors, such as Liquid Argon Time Projection Chambers (LAr TPC)
Performance of cryogenic charge readout electronics with the ARGONTUBE LAr TPC
ARGONTUBE is a liquid argon time projection chamber (TPC) with an electron drift length of up to 5 m equipped with cryogenic charge-sensitive preamplifiers. In this work, we
present results on its performance, including a comparison of the new cryogenic charge-sensitive preamplifiers with the previously used room-temperature-operated charge preamplifiers
A meta-analysis of individual participant data reveals an association between circulating levels of IGF-I and prostate cancer risk
The role of insulin-like growth factors (IGF) in prostate cancer development is not fully understood. To investigate the association between circulating concentrations of IGFs (IGF-I, IGF-II, IGFBP-1, IGFBP-2, and IGFBP-3) and prostate cancer risk, we pooled individual participant data from 17 prospective and two cross-sectional studies, including up to 10,554 prostate cancer cases and 13,618 control participants. Conditional logistic regression was used to estimate the ORs for prostate cancer based on the study-specific fifth of each analyte. Overall, IGF-I, IGF-II, IGFBP-2, and IGFBP-3 concentrations were positively associated with prostate cancer risk (Ptrend all ≤ 0.005), and IGFBP-1 was inversely associated weakly with risk (Ptrend = 0.05). However, heterogeneity between the prospective and cross-sectional studies was evident (Pheterogeneity = 0.03), unless the analyses were restricted to prospective studies (with the exception of IGF-II, Pheterogeneity = 0.02). For prospective studies, the OR for men in the highest versus the lowest fifth of each analyte was 1.29 (95% confidence interval, 1.16-1.43) for IGF-I, 0.81 (0.68-0.96) for IGFBP-1, and 1.25 (1.12-1.40) for IGFBP-3. These associations did not differ significantly by time-to-diagnosis or tumor stage or grade. After mutual adjustment for each of the other analytes, only IGF-I remained associated with risk. Our collaborative study represents the largest pooled analysis of the relationship between prostate cancer risk and circulating concentrations of IGF-I, providing strong evidence that IGF-I is highly likely to be involved in prostate cancer development
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