A Consistent Nonparametric Test for Causality in Quantile

This paper proposes a nonparametric test of causality in quantile. Zheng (1998) has proposed an idea to reduce the problem of testing a quantile restriction to a problem of testing a particular type of mean restriction in independent data. We extend Zheng’s approach to the case of dependent data, particularly to the test of Granger causality in quantile. The proposed test statistic is shown to have a second-order degenerate U-statistic as a leading term under the null hypothesis. Using the result on the asymptotic normal distribution for a general second order degenerate U-statistics with weakly dependent data of Fan and Li (1996), we establish the asymptotic distribution of the test statistic for causality in quantile under ß-mixing (absolutely regular) process.Granger Causality, Quantile, Nonparametric Test

Support Vector Regression Based GARCH Model with Application to Forecasting Volatility of Financial Returns

In recent years, support vector regression (SVR), a novel neural network (NN) technique, has been successfully used for financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH method is proposed and is compared with a moving average (MA), a recurrent NN and a parametric GACH in terms of their ability to forecast financial markets volatility. The real data in this study uses British Pound-US Dollar (GBP) daily exchange rates from July 2, 2003 to June 30, 2005 and New York Stock Exchange (NYSE) daily composite index from July 3, 2003 to June 30, 2005. The experiment shows that, under both varying and fixed forecasting schemes, the SVR-based GARCH outperforms the MA, the recurrent NN and the parametric GARCH based on the criteria of mean absolute error (MAE) and directional accuracy (DA). No structured way being available to choose the free parameters of SVR, the sensitivity of performance is also examined to the free parameters.recurrent support vector regression, GARCH model, volatility forecasting

A consistent nonparametric test for causality in quantile

This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.This paper proposes a nonparametric test of Granger causality in quantile. Zheng (1998, Econometric Theory 14, 123–138) studied the idea to reduce the problem of testing a quantile restriction to a problem of testing a particular type of mean restriction in independent data. We extend Zheng’s approach to the case of dependent data, particularly to the test of Granger causality in quantile. Combining the results of Zheng (1998) and Fan and Li (1999, Journal of Nonparametric Statistics 10, 245–271), we establish the asymptotic normal distribution of the test statistic under a β-mixing process. The test is consistent against all fixed alternatives and detects local alternatives approaching the null at proper rates. Simulations are carried out to illustrate the behavior of the test under the null and also the power of the test under plausible alternatives. An economic application considers the causal relations between the crude oil price, the USD/GBP exchange rate, and the gold price in the gold market.Peer Reviewe

Enhanced osteogenesis of human urine-derived stem cells by direct delivery of 30Kc19α–Lin28A protein

Urine-derived stem cells (USCs) are a promising source for regenerative medicine because of their advantages such as easy and non-invasive collection from the human body, stable expansion, and the potential to differentiate into multiple lineages, including osteoblasts. In this study, we propose a strategy to enhance the osteogenic potential of human USCs using Lin28A, a transcription factor that inhibits let-7 miRNA processing. To address concerns regarding the safety of foreign gene integration and potential risk of tumorigenicity, we intracellularly delivered Lin28A as a recombinant protein fused with a cell-penetrating and protein-stabilizing protein, 30Kc19α. 30Kc19α–Lin28A fusion protein exhibited improved thermal stability and was delivered into USCs without significant cytotoxicity. 30Kc19α–Lin28A treatment elevated calcium deposition and upregulated several osteoblast-specific gene expressions in USCs derived from multiple donors. Our results indicate that intracellularly delivered 30Kc19α–Lin28A enhances the osteoblastic differentiation of human USCs by affecting the transcriptional regulatory network involved in metabolic reprogramming and stem cell potency. Therefore, 30Kc19α–Lin28A may provide a technical advancement toward developing clinically feasible strategies for bone regeneration

Simultaneous Intrinsic and Extrinsic Parameter Identification of a Hand-Mounted Laser-Vision Sensor

In this paper, we propose a simultaneous intrinsic and extrinsic parameter identification of a hand-mounted laser-vision sensor (HMLVS). A laser-vision sensor (LVS), consisting of a camera and a laser stripe projector, is used as a sensor component of the robotic measurement system, and it measures the range data with respect to the robot base frame using the robot forward kinematics and the optical triangulation principle. For the optimal estimation of the model parameters, we applied two optimization techniques: a nonlinear least square optimizer and a particle swarm optimizer. Best-fit parameters, including both the intrinsic and extrinsic parameters of the HMLVS, are simultaneously obtained based on the least-squares criterion. From the simulation and experimental results, it is shown that the parameter identification problem considered was characterized by a highly multimodal landscape; thus, the global optimization technique such as a particle swarm optimization can be a promising tool to identify the model parameters for a HMLVS, while the nonlinear least square optimizer often failed to find an optimal solution even when the initial candidate solutions were selected close to the true optimum. The proposed optimization method does not require good initial guesses of the system parameters to converge at a very stable solution and it could be applied to a kinematically dissimilar robot system without loss of generality

Inhibitory effect of 4-O-methylhonokiol on lipopolysaccharide-induced neuroinflammation, amyloidogenesis and memory impairment via inhibition of nuclear factor-kappaB in vitro and in vivo models

<p>Abstract</p> <p>Background</p> <p>Neuroinflammation is important in the pathogenesis and progression of Alzheimer disease (AD). Previously, we demonstrated that lipopolysaccharide (LPS)-induced neuroinflammation caused memory impairments. In the present study, we investigated the possible preventive effects of 4-<it>O</it>-methylhonokiol, a constituent of <it>Magnolia officinalis</it>, on memory deficiency caused by LPS, along with the underlying mechanisms.</p> <p>Methods</p> <p>We investigated whether 4-<it>O</it>-methylhonokiol (0.5 and 1 mg/kg in 0.05% ethanol) prevents memory dysfunction and amyloidogenesis on AD model mice by intraperitoneal LPS (250 μg/kg daily 7 times) injection. In addition, LPS-treated cultured astrocytes and microglial BV-2 cells were investigated for anti-neuroinflammatory and anti-amyloidogenic effect of 4-<it>O</it>-methylhonkiol (0.5, 1 and 2 μM).</p> <p>Results</p> <p>Oral administration of 4-<it>O</it>-methylhonokiol ameliorated LPS-induced memory impairment in a dose-dependent manner. In addition, 4-<it>O</it>-methylhonokiol prevented the LPS-induced expression of inflammatory proteins; inducible nitric oxide synthase (iNOS) and cyclooxygenase-2 (COX-2) as well as activation of astrocytes (expression of glial fibrillary acidic protein; GFAP) in the brain. In <it>in vitro </it>study, we also found that 4-<it>O</it>-methylhonokiol suppressed the expression of iNOS and COX-2 as well as the production of reactive oxygen species, nitric oxide, prostaglandin E₂, tumor necrosis factor-α, and interleukin-1β in the LPS-stimulated cultured astrocytes. 4-<it>O</it>-methylhonokiol also inhibited transcriptional and DNA binding activity of NF-κB via inhibition of IκB degradation as well as p50 and p65 translocation into nucleus of the brain and cultured astrocytes. Consistent with the inhibitory effect on neuroinflammation, 4-<it>O</it>-methylhonokiol inhibited LPS-induced Aβ_1-42generation, β- and γ-secretase activities, and expression of amyloid precursor protein (APP), BACE1 and C99 as well as activation of astrocytes and neuronal cell death in the brain, in cultured astrocytes and in microglial BV-2 cells.</p> <p>Conclusion</p> <p>These results suggest that 4-<it>O</it>-methylhonokiol inhibits LPS-induced amyloidogenesis via anti-inflammatory mechanisms. Thus, 4-<it>O</it>-methylhonokiol can be a useful agent against neuroinflammation-associated development or the progression of AD.</p

A Consistent Nonparametric Test for Causality in Quantile

This paper proposes a nonparametric test of causality in quantile. Zheng (1998) has proposed an idea to reduce the problem of testing a quantile restriction to a problem of testing a particular type of mean restriction in independent data. We extend Zheng’s approach to the case of dependent data, particularly to the test of Granger causality in quantile. The proposed test statistic is shown to have a second-order degenerate U-statistic as a leading term under the null hypothesis. Using the result on the asymptotic normal distribution for a general second order degenerate U-statistics with weakly dependent data of Fan and Li (1996), we establish the asymptotic distribution of the test statistic for causality in quantile under β-mixing (absolutely regular) process

Support Vector Regression Based GARCH Model withApplication to Forecasting Volatility of Financial Returns

In recent years, support vector regression (SVR), a novel neural network (NN) technique, has been successfully used for financial forecasting. This paper deals with the application of SVR in volatility forecasting. Based on a recurrent SVR, a GARCH method is proposed and is compared with a moving average (MA), a recurrent NN and a parametric GACH in terms of their ability to forecast financial markets volatility. The real data in this study uses British Pound-US Dollar (GBP) daily exchange rates from July 2, 2003 to June 30, 2005 and New York Stock Exchange (NYSE) daily composite index from July 3, 2003 to June 30, 2005. The experiment shows that, under both varying and fixed forecasting schemes, the SVR-based GARCH outperforms the MA, the recurrent NN and the parametric GARCH based on the criteria of mean absolute error (MAE) and directional accuracy (DA). No structured way being available to choose the free parameters of SVR, the sensitivity of performance is also examined to the free parameters

A consistent nonparametric test for nonlinear causality—Specification in time series regression

Since the pioneering work by Granger (1969), many authors have proposed tests of causality between economic time series. Most of them are concerned only with “linear causality in mean”, or if a series linearly affects the (conditional) mean of the other series. It is no doubt of primary interest, but dependence between series may be nonlinear, and/or not only through the conditional mean. Indeed conditional heteroskedastic models are widely studied recently. The purpose of this paper is to propose a nonparametric test for possibly nonlinear causality. Taking into account that dependence in higher order moments are becoming an important issue especially in financial time series, we also consider a test for causality up to the K-th conditional moment. Statistically, we can also view this test as a nonparametric omitted variable test in time series regression. A desirable property of the test is that it has nontrivial power against T[1/2]-local alternatives, where T is the sample size. Also, we can form a test statistic accordingly if we have some knowledge on the alternative hypothesis. Furthermore, we show that the test statistic includes most of the omitted variable test statistics as special cases asymptotically. The null asymptotic distribution is not normal, but we can easily calculate the critical regions by simulation. Monte Carlo experiments show that the proposed test has good size and power properties