75 research outputs found

    High-dimensional Linear Regression for Dependent Data with Applications to Nowcasting

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    Recent research has focused on â„“1\ell_1 penalized least squares (Lasso) estimators for high-dimensional linear regressions in which the number of covariates pp is considerably larger than the sample size nn. However, few studies have examined the properties of the estimators when the errors and/or the covariates are serially dependent. In this study, we investigate the theoretical properties of the Lasso estimator for a linear regression with a random design and weak sparsity under serially dependent and/or nonsubGaussian errors and covariates. In contrast to the traditional case, in which the errors are independent and identically distributed and have finite exponential moments, we show that pp can be at most a power of nn if the errors have only finite polynomial moments. In addition, the rate of convergence becomes slower owing to the serial dependence in the errors and the covariates. We also consider the sign consistency of the model selection using the Lasso estimator when there are serial correlations in the errors or the covariates, or both. Adopting the framework of a functional dependence measure, we describe how the rates of convergence and the selection consistency of the estimators depend on the dependence measures and moment conditions of the errors and the covariates. Simulation results show that a Lasso regression can be significantly more powerful than a mixed-frequency data sampling regression (MIDAS) and a Dantzig selector in the presence of irrelevant variables. We apply the results obtained for the Lasso method to nowcasting with mixed-frequency data, in which serially correlated errors and a large number of covariates are common. The empirical results show that the Lasso procedure outperforms the MIDAS regression and the autoregressive model with exogenous variables in terms of both forecasting and nowcasting

    Tensor Factor Model Estimation by Iterative Projection

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    Tensor time series, which is a time series consisting of tensorial observations, has become ubiquitous. It typically exhibits high dimensionality. One approach for dimension reduction is to use a factor model structure, in a form similar to Tucker tensor decomposition, except that the time dimension is treated as a dynamic process with a time dependent structure. In this paper we introduce two approaches to estimate such a tensor factor model by using iterative orthogonal projections of the original tensor time series. The approaches extend the existing estimation procedures and our theoretical investigation shows that they improve the estimation accuracy and convergence rate significantly. The developed approaches are similar to higher order orthogonal projection methods for tensor decomposition, but with significant differences and theoretical properties. Simulation study is conducted to further illustrate the statistical properties of these estimators

    Fiber-optic refractometer based on a phase-shifted fiber Bragg grating on a side-hole fiber

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    A fiber-optic refractive index (RI) sensor based on a π-phaseshifted fiber-Bragg-grating (πFBG) inscribed on a side-hole fiber is presented. The reflection spectrum of the πFBG features two narrow notches associated with the two polarization modes and the spectral spacing of the notches is used for high-sensitivity RI sensing with little temperature cross-sensitivity. The side-hole fiber maintains its outer diameter and mechanical strength. The side-hole fiber is also naturally integrated into a microfluidic system for convenient sample delivery and reduced sample amount. A novel demodulation method based on laser frequency modulation to enhance the sensor dynamic range is proposed and demonstrated

    Enhanced polarization and abnormal flexural deformation in bent freestanding perovskite oxides

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    Recent realizations of ultrathin freestanding perovskite oxides offer a unique platform to probe novel properties in two-dimensional oxides. Here, we observe a giant flexoelectric response in freestanding BiFeO3 and SrTiO3 in their bent state arising from strain gradients up to 3.5 × 107 m−1, suggesting a promising approach for realizing ultra-large polarizations. Additionally, a substantial change in membrane thickness is discovered in bent freestanding BiFeO3, which implies an unusual bending-expansion/shrinkage effect in the ferroelectric membrane that has never been seen before in crystalline materials. Our theoretical model reveals that this unprecedented flexural deformation within the membrane is attributable to a flexoelectricity–piezoelectricity interplay. The finding unveils intriguing nanoscale electromechanical properties and provides guidance for their practical applications in flexible nanoelectromechanical systems

    Finishing the euchromatic sequence of the human genome

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    The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead
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