Testing for pure-jump processes for high-frequency data

Pure-jump processes have been increasingly popular in modeling high-frequency financial data, partially due to their versatility and flexibility. In the meantime, several statistical tests have been proposed in the literature to check the validity of using pure-jump models. However, these tests suffer from several drawbacks, such as requiring rather stringent conditions and having slow rates of convergence. In this paper, we propose a different test to check whether the underlying process of high-frequency data can be modeled by a pure-jump process. The new test is based on the realized characteristic function, and enjoys a much faster convergence rate of order $O(n^{1/2})$ (where $n$ is the sample size) versus the usual $o(n^{1/4})$ available for existing tests; it is applicable much more generally than previous tests; for example, it is robust to jumps of infinite variation and flexible modeling of the diffusion component. Simulation studies justify our findings and the test is also applied to some real high-frequency financial data.Comment: Published at http://dx.doi.org/10.1214/14-AOS1298 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Robust Statistical Inference for Large-dimensional Matrix-valued Time Series via Iterative Huber Regression

Matrix factor model is drawing growing attention for simultaneous two-way dimension reduction of well-structured matrix-valued observations. This paper focuses on robust statistical inference for matrix factor model in the ``diverging dimension" regime. We derive the convergence rates of the robust estimators for loadings, factors and common components under finite second moment assumption of the idiosyncratic errors. In addition, the asymptotic distributions of the estimators are also derived under mild conditions. We propose a rank minimization and an eigenvalue-ratio method to estimate the pair of factor numbers consistently. Numerical studies confirm the iterative Huber regression algorithm is a practical and reliable approach for the estimation of matrix factor model, especially under the cases with heavy-tailed idiosyncratic errors . We illustrate the practical usefulness of the proposed methods by two real datasets, one on financial portfolios and one on the macroeconomic indices of China

Nonparametric Regression With Nearly Integrated Regressors Under Long Run Dependence

We study nonparametric estimation of regression function with nonstationary (integrated or nearly integrated) covariates and the error series of the regressor process following a fractional ARIMA model. A local linear estimation method is developed to estimate the unknown regression function. The asymptotic results of the resulting estimator at both interior points and boundaries are obtained. The asymptotic distribution is mixed normal, associated with the local time of an Ornstein-Uhlenbeck (O-U) fractional Brownian motion. Furthermore, we study the Nadaraya-Watson estimator and examine its asymptotic results. As a result, it shares exactly the same asymptotic results as those for the local linear estimator for the zero energy situation. But for the non-zero energy case, the local linear estimator is superior over the Nadaraya-Watson estimator in terms of optimal convergence rate. Moreover, a comparison of our results with the conventional results for stationary covariates is presented. Finally, a Monte Carlo simulation is conducted to illustrate the finite sample performance of the proposed estimator.

Discovery of six high-redshift quasars with the Lijiang 2.4m telescope and the Multiple Mirror Telescope

Quasars with redshifts greater than 4 are rare, and can be used to probe the structure and evolution of the early universe. Here we report the discovery of six new quasars with $i$-band magnitudes brighter than 19.5 and redshifts between 2.4 and 4.6 from the YFOSC spectroscopy of the Lijiang 2.4m telescope in February, 2012. These quasars are in the list of $z>3.6$ quasar candidates selected by using our proposed $J-K/i-Y$ criterion and the photometric redshift estimations from the SDSS optical and UKIDSS near-IR photometric data. Nine candidates were observed by YFOSC, and five among six new quasars were identified as $z>3.6$ quasars. One of the other three objects was identified as a star and the other two were unidentified due to the lower signal-to-noise ratio of their spectra. This is the first time that $z>4$ quasars have been discovered using a telescope in China. Thanks to the Chinese Telescope Access Program (TAP), the redshift of 4.6 for one of these quasars was confirmed by the Multiple Mirror Telescope (MMT) Red Channel spectroscopy. The continuum and emission line properties of these six quasars, as well as their central black hole masses and Eddington ratios, were obtained.Comment: 7 pages, 2 figures, published in Research in Astronomy and Astrophysics (RAA) as a lette

Projected Rotational Velocities for LAMOST Stars with Effective Temperatures Lower than 9000 K

© 2024 The Author(s). Published by the American Astronomical Society. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/In Data Release 9 of LAMOST, we present measurements of v sin i for a total of 121,698 stars measured using the Medium Resolution Spectrograph (MRS) and 80,108 stars using the Low Resolution Spectrograph (LRS). These values were obtained through a χ 2 minimization process, comparing LAMOST spectra with corresponding grids of synthetically broadened spectra. Due to the resolution and the spectral range of LAMOST, v sin i measurements are limited to stars with an effective temperature (T eff) ranging from 5000 to 8500 K for MRS and 7000 to 9000 K for LRS. The detectable v sin i for MRS is set between 27 and 350 km s−1, and for LRS between 110 and 350 km s−1. This limitation is because the convolved reference spectra become less informative beyond 350 km s−1. The intrinsic precision of v sin i, determined from multiepoch observations, is approximately ∼4.0 km s−1 for MRS and ∼10.0 km s−1 for LRS at a signal-to-noise ratio greater than 50. Our v sin i values show consistency with those from APOGEE17, displaying a scatter of 8.79 km s−1. They are also in agreement with measurements from the Gaia DR3 and Sun et al. catalogs. An observed trend in LAMOST MRS data is the decrease in v sin i with a drop in T eff, particularly transiting around 7000 K for dwarfs and 6500 K for giants, primarily observed in stars with near-solar abundances.Peer reviewe

A Point-of-Care Sensing Platform for Multiplexed Detection of Chronic Kidney Disease Biomarkers Using Molecularly Imprinted Polymers

Chronic kidney disease (CKD) is one of the most serious non-communicable diseases affecting the population. In the early-stages patients have no obvious symptoms, until it becomes life-threatening leading end-stage kidney failure. Therefore, it is important to early diagnose CKD to allow therapeutic interventions and progression monitoring. Here, a point-of-care (POC) sensing platform is reported for the simultaneous detection of three CKD biomarkers, namely creatinine, urea, and human serum albumin (HSA), using reduced graphene oxide/polydopamine-molecularly imprinted polymer (rGO/PDA-MIP) fabricated with novel surface-molecularly imprinting technology. A multi-channel electrochemical POC readout system with differential pulse voltammetry (DPV) function is developed, allowing the simultaneous detection of the three biomarkers, in combination with the surface-MIP electrodes. This sensing platform achieves the record low limit-of-detection (LoD) at a femtomolar level for all three analytes, with wide detection ranges covering their physiological concentrations. Clinical validation is performed by measuring these analytes in serum and urine from healthy controls and patients with CKD. The average recovery rate is 81.8–119.1% compared to the results obtained from the hospital, while this platform is more cost-effective, user-friendly, and requires less sample-to-result time, showing the potential to be deployed in resource-limited settings for the early diagnosis and tracking progression of CKD

Gut phageome: challenges in research and impact on human microbiota

The human gut microbiome plays a critical role in maintaining our health. Fluctuations in the diversity and structure of the gut microbiota have been implicated in the pathogenesis of several metabolic and inflammatory conditions. Dietary patterns, medication, smoking, alcohol consumption, and physical activity can all influence the abundance of different types of microbiota in the gut, which in turn can affect the health of individuals. Intestinal phages are an essential component of the gut microbiome, but most studies predominantly focus on the structure and dynamics of gut bacteria while neglecting the role of phages in shaping the gut microbiome. As bacteria-killing viruses, the distribution of bacteriophages in the intestine, their role in influencing the intestinal microbiota, and their mechanisms of action remain elusive. Herein, we present an overview of the current knowledge of gut phages, their lifestyles, identification, and potential impact on the gut microbiota