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 $Z_2\times CP$ in both neutrino and charged lepton sectors. All lepton mixing angles and CP violation phases are predicted to depend on two free parameters $\theta_{l}$ and $\theta_{\nu}$ varying in the range of $[0, \pi)$. As an example, we comprehensively study the lepton mixing patterns which can be derived from the flavor group $\Delta(6n^2)$ 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 $\theta_{u}$ and $\theta_{d}$. A simultaneous description of quark and lepton flavor mixing structures can be achieved from a common flavor group $\Delta(6n^2)$ and CP, and accordingly the smallest value of the group index $n$ is $n=7$.Comment: 40 pages, 8 figure

Estimating Mean Survival Time: When is it Possible?

For right‐censored survival data, it is well‐known that the mean survival time can be consistently estimated when the support of the censoring time contains the support of the survival time. In practice, however, this condition can be easily violated because the follow‐up of a study is usually within a finite window. In this article, we show that the mean survival time is still estimable from a linear model when the support of some covariate(s) with non‐zero coefficient(s) is unbounded regardless of the length of follow‐up. This implies that the mean survival time can be well estimated when the support of linear predictor is wide in practice. The theoretical finding is further verified for finite samples by simulation studies. Simulations also show that, when both models are correctly specified, the linear model yields reasonable mean square prediction errors and outperforms the Cox model, particularly with heavy censoring and short follow‐up time.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111162/1/sjos12112-sup-0001-documentS1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/111162/2/sjos12112.pd