Component selection and smoothing in multivariate nonparametric regression

We propose a new method for model selection and model fitting in multivariate nonparametric regression models, in the framework of smoothing spline ANOVA. The ``COSSO'' is a method of regularization with the penalty functional being the sum of component norms, instead of the squared norm employed in the traditional smoothing spline method. The COSSO provides a unified framework for several recent proposals for model selection in linear models and smoothing spline ANOVA models. Theoretical properties, such as the existence and the rate of convergence of the COSSO estimator, are studied. In the special case of a tensor product design with periodic functions, a detailed analysis reveals that the COSSO does model selection by applying a novel soft thresholding type operation to the function components. We give an equivalent formulation of the COSSO estimator which leads naturally to an iterative algorithm. We compare the COSSO with MARS, a popular method that builds functional ANOVA models, in simulations and real examples. The COSSO method can be extended to classification problems and we compare its performance with those of a number of machine learning algorithms on real datasets. The COSSO gives very competitive performance in these studies.Comment: Published at http://dx.doi.org/10.1214/009053606000000722 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

CP-Violation in the Two Higgs Doublet Model: from the LHC to EDMs

We study the prospective sensitivity to CP-violating Two Higgs Doublet Models from the 14 TeV LHC and future electric dipole moment (EDM) experiments. We concentrate on the search for a resonant heavy Higgs that decays to a $Z$ boson and a SM-like Higgs h, leading to the $Z(\ell\ell)h(b\bar{b})$ final state. The prospective LHC reach is analyzed using the Boosted Decision Tree method. We illustrate the complementarity between the LHC and low energy EDM measurements and study the dependence of the physics reach on the degree of deviation from the alignment limit. In all cases, we find that there exists a large part of parameter space that is sensitive to both EDMs and LHC searches.Comment: 21 pages, 34 figure

Dimensions of fractals related to languages defined by tagged strings in complete genomes

A representation of frequency of strings of length K in complete genomes of many organisms in a square has led to seemingly self-similar patterns when K increases. These patterns are caused by under-represented strings with a certain "tag"-string and they define some fractals when K tends to infinite. The Box and Hausdorff dimensions of the limit set are discussed. Although the method proposed by Mauldin and Williams to calculate Box and Hausdorff dimension is valid in our case, a different and simpler method is proposed in this paper.Comment: 9 pages with two figure

Congestion Control for Machine-Type Communications in LTE-A Networks

Collecting data from a tremendous amount of Internet-of-Things (IoT) devices for next generation networks is a big challenge. A large number of devices may lead to severe congestion in Radio Access Network (RAN) and Core Network (CN). 3GPP has specified several mechanisms to handle the congestion caused by massive amounts of devices. However, detailed settings and strategies of them are not defined in the standards and are left for operators. In this paper, we propose two congestion control algorithms which efficiently reduce the congestion. Simulation results demonstrate that the proposed algorithms can achieve 20~40% improvement regarding accept ratio, overload degree and waiting time compared with those in LTE-A