2,593 research outputs found
A sieve M-theorem for bundled parameters in semiparametric models, with application to the efficient estimation in a linear model for censored data
In many semiparametric models that are parameterized by two types of
parameters---a Euclidean parameter of interest and an infinite-dimensional
nuisance parameter---the two parameters are bundled together, that is, the
nuisance parameter is an unknown function that contains the parameter of
interest as part of its argument. For example, in a linear regression model for
censored survival data, the unspecified error distribution function involves
the regression coefficients. Motivated by developing an efficient estimating
method for the regression parameters, we propose a general sieve M-theorem for
bundled parameters and apply the theorem to deriving the asymptotic theory for
the sieve maximum likelihood estimation in the linear regression model for
censored survival data. The numerical implementation of the proposed estimating
method can be achieved through the conventional gradient-based search
algorithms such as the Newton--Raphson algorithm. We show that the proposed
estimator is consistent and asymptotically normal and achieves the
semiparametric efficiency bound. Simulation studies demonstrate that the
proposed method performs well in practical settings and yields more efficient
estimates than existing estimating equation based methods. Illustration with a
real data example is also provided.Comment: Published in at http://dx.doi.org/10.1214/11-AOS934 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Genotoxic effects of low 2.45 GHz microwave radiation exposures on Sprague Dawley rats
This paper investigates the genotoxic effects of 2.45 GHz microwave (MW) radiation exposure at low specific absorption rates (SAR). 200 Sprague Dawley rats were exposed to SAR values between 0.48 and 4.30 W.kg-1 and the DNA of different tissues extracted, precipitated and quantified. Induced deoxyribonucleic acid (DNA) damages were assessed using the methods of DNA Direct Amplification of Length Polymorphisms (DALP) and the Single Cell Gel Electrophoresis (SCGE). Densitometric gel analysis demonstrated distinctly altered band patterns within the range of 40 and 120 bp in exposed samples and in the tail DNA of the same animals before exposure compared with control. Results were re-affirmed with SCGE (comet assay) for the same cells. Different tissues had different sensitivities to exposures with the brains having the highest. DNA damages were sex-independent. There was
statistically significant difference in the Olive moment and % DNA in the tail of the exposed tissues compared with control (p < 0.05). Observed effects were attributed to magnetic field interactions and production of reactive oxygen species. We conclude that low SAR 2.45 GHz MW radiation exposures can induce DNA single strand breaks and the direct genome analysis of DNA of various tissues
demonstrated potential for genotoxicity
Probabilistic Label Relation Graphs with Ising Models
We consider classification problems in which the label space has structure. A
common example is hierarchical label spaces, corresponding to the case where
one label subsumes another (e.g., animal subsumes dog). But labels can also be
mutually exclusive (e.g., dog vs cat) or unrelated (e.g., furry, carnivore). To
jointly model hierarchy and exclusion relations, the notion of a HEX (hierarchy
and exclusion) graph was introduced in [7]. This combined a conditional random
field (CRF) with a deep neural network (DNN), resulting in state of the art
results when applied to visual object classification problems where the
training labels were drawn from different levels of the ImageNet hierarchy
(e.g., an image might be labeled with the basic level category "dog", rather
than the more specific label "husky"). In this paper, we extend the HEX model
to allow for soft or probabilistic relations between labels, which is useful
when there is uncertainty about the relationship between two labels (e.g., an
antelope is "sort of" furry, but not to the same degree as a grizzly bear). We
call our new model pHEX, for probabilistic HEX. We show that the pHEX graph can
be converted to an Ising model, which allows us to use existing off-the-shelf
inference methods (in contrast to the HEX method, which needed specialized
inference algorithms). Experimental results show significant improvements in a
number of large-scale visual object classification tasks, outperforming the
previous HEX model.Comment: International Conference on Computer Vision (2015
Toward a unified interpretation of quark and lepton mixing from flavor and CP symmetries
We discussed the scenario that a discrete flavor group combined with CP
symmetry is broken to in both neutrino and charged lepton
sectors. All lepton mixing angles and CP violation phases are predicted to
depend on two free parameters and varying in the
range of . As an example, we comprehensively study the lepton mixing
patterns which can be derived from the flavor group and CP
symmetry. Three kinds of phenomenologically viable lepton mixing matrices are
obtained up to row and column permutations. We further extend this approach to
the quark sector. The precisely measured quark mixing angles and CP invariant
can be accommodated for certain values of the free parameters and
. A simultaneous description of quark and lepton flavor mixing
structures can be achieved from a common flavor group and CP,
and accordingly the smallest value of the group index is .Comment: 40 pages, 8 figure
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