Efficient Estimation of Copula-based Semiparametric Markov Models

This paper considers efficient estimation of copula-based semiparametric strictly stationary Markov models. These models are characterized by nonparametric invariant (one-dimensional marginal) distributions and parametric bivariate copula functions; where the copulas capture temporal dependence and tail dependence of the processes. The Markov processes generated via tail dependent copulas may look highly persistent and are useful for financial and economic applications. We first show that Markov processes generated via Clayton, Gumbel and Student's $t$ copulas and their survival copulas are all geometrically ergodic. We then propose a sieve maximum likelihood estimation (MLE) for the copula parameter, the invariant distribution and the conditional quantiles. We show that the sieve MLEs of any smooth functionals are root-$n$ consistent, asymptotically normal and efficient; and that their sieve likelihood ratio statistics are asymptotically chi-square distributed. We present Monte Carlo studies to compare the finite sample performance of the sieve MLE, the two-step estimator of Chen and Fan (2006), the correctly specified parametric MLE and the incorrectly specified parametric MLE. The simulation results indicate that our sieve MLEs perform very well; having much smaller biases and smaller variances than the two-step estimator for Markov models generated via Clayton, Gumbel and other tail dependent copulas.Copula, Tail dependence, Nonlinear Markov models, Geometric ergodicity, Sieve MLE, Semiparametric efficiency, Sieve likelihood ratio statistics, Value-at-Risk

Measurements of diffusion, T 1 and T 2 in one shot by MMME

In this paper, we demonstrate a rapid simultaneous measurement of diffusion constant D, T1 and T2 relaxation times in just two scans. Theoretical standard deviations of D, T1 T2 for a wide range of T1 and T2 were predicted for given sequences with a random experimental error of 3%. By carefully selecting of sequence parameters for samples with different relaxation times, the error propagators in T1, T2, and D can be modified to within 10%

Efficient Estimation of Multivariate Semi-nonparametric GARCH Filtered Copula Models

This paper considers estimation of semi-nonparametric GARCH filtered copula models in which the individual time series are modelled by semi-nonparametric GARCH and the joint distributions of the multivariate standardized innovations are characterized by parametric copulas with nonparametric marginal distributions. The models extend those of Chen and Fan (2006) to allow for semi-nonparametric conditional means and volatilities, which are estimated via the method of sieves such as splines. The fitted residuals are then used to estimate the copula parameters and the marginal densities of the standardized innovations jointly via the sieve maximum likelihood (SML). We show that, even using nonparametrically filtered data, both our SML and the two-step copula estimator of Chen and Fan (2006) are still root-n consistent and asymptotically normal, and the asymptotic variances of both estimators do not depend on the nonparametric filtering errors. Even more surprisingly, our SML copula estimator using the filtered data achieves the full semiparametric efficiency bound as if the standardized innovations were directly observed. These nice properties lead to simple and more accurate estimation of Value-at-Risk (VaR) for multivariate financial data with flexible dynamics, contemporaneous tail dependence and asymmetric distributions of innovations. Monte Carlo studies demonstrate that our SML estimators of the copula parameters and the marginal distributions of the standardized innovations have smaller variances and smaller mean squared errors compared to those of the two-step estimators in finite samples. A real data application is presented

Semiparametric estimation of the bid-ask spread in extended roll models

We propose new methods for estimating the bid-ask spread from observed transaction prices alone. Our methods are based on the empirical characteristic function. We compare our methods theoretically and numerically with the Roll (1984) method as well as with its best known competitor, the Hasbrouck (2004) method, and find that our estimators perform much better when this distribution is far from Gaussian. Our methods are applied to the E-mini futures con- tract on the S&P 500 during the Flash Crash of May 6, 2010. We also establish √T consistency and asymptotic normality of the proposed estimators in various extended Roll models.non

Efficient estimation of copula-based semiparametric Markov models

This paper considers the efficient estimation of copula-based semiparametric strictly stationary Markov models. These models are characterized by nonparametric invariant (one-dimensional marginal) distributions and parametric bivariate copula functions where the copulas capture temporal dependence and tail dependence of the processes. The Markov processes generated via tail dependent copulas may look highly persistent and are useful for financial and economic applications. We first show that Markov processes generated via Clayton, Gumbel and Student's $t$ copulas and their survival copulas are all geometrically ergodic. We then propose a sieve maximum likelihood estimation (MLE) for the copula parameter, the invariant distribution and the conditional quantiles. We show that the sieve MLEs of any smooth functional is root-$n$ consistent, asymptotically normal and efficient and that their sieve likelihood ratio statistics are asymptotically chi-square distributed. Monte Carlo studies indicate that, even for Markov models generated via tail dependent copulas and fat-tailed marginals, our sieve MLEs perform very well.Comment: Published in at http://dx.doi.org/10.1214/09-AOS719 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Confidence-and-Refinement Adaptation Model for Cross-Domain Semantic Segmentation

With the rapid development of convolutional neural networks (CNNs), significant progress has been achieved in semantic segmentation. Despite the great success, such deep learning approaches require large scale real-world datasets with pixel-level annotations. However, considering that pixel-level labeling of semantics is extremely laborious, many researchers turn to utilize synthetic data with free annotations. But due to the clear domain gap, the segmentation model trained with the synthetic images tends to perform poorly on the real-world datasets. Unsupervised domain adaptation (UDA) for semantic segmentation recently gains an increasing research attention, which aims at alleviating the domain discrepancy. Existing methods in this scope either simply align features or the outputs across the source and target domains or have to deal with the complex image processing and post-processing problems. In this work, we propose a novel multi-level UDA model named Confidence-and-Refinement Adaptation Model (CRAM), which contains a confidence-aware entropy alignment (CEA) module and a style feature alignment (SFA) module. Through CEA, the adaptation is done locally via adversarial learning in the output space, making the segmentation model pay attention to the high-confident predictions. Furthermore, to enhance the model transfer in the shallow feature space, the SFA module is applied to minimize the appearance gap across domains. Experiments on two challenging UDA benchmarks ``GTA5-to-Cityscapes'' and ``SYNTHIA-to-Cityscapes'' demonstrate the effectiveness of CRAM. We achieve comparable performance with the existing state-of-the-art works with advantages in simplicity and convergence speed

Medium-Term Visual Outcomes of Apodized Diffractive Multifocal Intraocular Lens with +3.00 D Addition Power

Purpose. To evaluate 2-year visual acuities and questionnaire after bilateral implantation of SN6AD1 multifocal intraocular lens (MIOL) or SN60WF IOL. Methods. Patients randomly scheduled for bilateral implantation of SN6AD1 MIOL and SN60WF IOL with 2-year follow-up were enrolled. Uncorrected/corrected distance and near visual acuity, uncorrected intermediate visual acuity at 63 cm under high and low contrast, reading activity, the defocus curve, and a quality-of-life questionnaire were evaluated. Results. Each group comprised 20 patients. Uncorrected intermediate visual acuities and uncorrected near visual acuity were better in SN6AD1 group than in SN60WF group (P = 0.005, P = 0.011, and P < 0.001). In SN6AD1 group, the uncorrected intermediate and near visual acuities 1 year and 2 years postoperatively were reduced than postoperative 3-month outcomes, respectively. SN6AD1 group reported superior overall spectacle independence and inferior satisfaction. SN6AD1 group had a longer reading newspaper duration than SN60WF group (P = 0.036). When using mobile phone, SN6AD1 group had a more comfortable distance than SN60WF group (P < 0.001) and higher speed of reading fixed text message (P < 0.001). Conclusion. SN6AD1 MIOL provided a satisfactory full range of visual acuities and questionnaire performance 2 years postoperatively. One-year and 2-year uncorrected near and intermediate visual acuities of SN6AD1 MIOL were lower than those 3 months postoperatively

Cellular senescence contributes to radiation-induced hyposalivation by affecting the stem/progenitor cell niche

Radiotherapy for head and neck cancer is associated with impairment of salivary gland function and consequent xerostomia, which has a devastating effect on the quality of life of the patients. The mechanism of radiation-induced salivary gland damage is not completely understood. Cellular senescence is a permanent state of cell cycle arrest accompanied by a secretory phenotype which contributes to inflammation and tissue deterioration. Genotoxic stresses, including radiation-induced DNA damage, are known to induce a senescence response. Here, we show that radiation induces cellular senescence preferentially in the salivary gland stem/progenitor cell niche of mouse models and patients. Similarly, salivary gland-derived organoids show increased expression of senescence markers and pro-inflammatory senescence-associated secretory phenotype (SASP) factors after radiation exposure. Clearance of senescent cells by selective removal of p16Ink4a-positive cells by the drug ganciclovir or the senolytic drug ABT263 lead to increased stem cell self-renewal capacity as measured by organoid formation efficiency. Additionally, pharmacological treatment with ABT263 in mice irradiated to the salivary glands mitigates tissue degeneration, thus preserving salivation. Our data suggest that senescence in the salivary gland stem/progenitor cell niche contributes to radiation-induced hyposalivation. Pharmacological targeting of senescent cells may represent a therapeutic strategy to prevent radiotherapy-induced xerostomia