A segmentation procedure of the pervious area of the aorta artery from CT images without contrast medium

In this paper, we develop an algorithm for the segmentation of the pervious lumen of the aorta artery in computed tomography (CT) images without contrast medium, a challenging task due to the closeness gray levels of the different zones to segment. The novel approach of the proposed procedure mainly resides in enhancing the resolution of the image by the application of the algorithm deduced from the mathematical theory of sampling Kantorovich operators. After the application of suitable digital image processing techniques, the pervious zone of the artery can be distinguished from the occluded one. Numerical tests have been performed using 233 CT images, and suitable numerical errors have been computed and introduced ex novo to evaluate the performance of the proposed method. The above procedure is completely automatic in all its parts after the initial region of interest (ROI) selection. The main advantages of this approach relies in the potential possibility of performing diagnosis concerning vascular pathologies even for patients with severe kidney diseases or allergic problems, for which CT images with contrast medium cannot be achieved.     

可变抽样区间的多变量自相关过程VAR控制图

基于批量-均值法的思想,向量自回归(VAR)控制图对多变量自相关过程的较小偏移可以进行有效控制。为了提高多变量自相关过程监控效率,本文研究可变抽样区间的VAR控制图。首先,对多变量自相关过程的VAR控制图进行可变抽样区间设计;然后,用蒙特卡洛模拟方法计算其平均报警时间;最后,以平均报警时间为评价准则,对所设计的可变抽样区间VAR控制图与固定抽样区间的VAR控制图进行比较研究。研究结果表明:所设计的可变抽样区间多变量自相关过程VAR控制图较固定抽样区间的多变量自相关过程VAR控制图能更好的监控过程的变化。     

非仿射随机波动率的欧式障碍期权定价

研究非仿射随机波动率模型的欧式障碍期权定价问题时,首先介绍了非仿射随机波动率模型,其次利用投资组合和It^o引理,得到了该模型下扩展的Black-Schole偏微分方程.由于这个方程没有显示解,因此采用对偶蒙特卡罗模拟法计算欧式障碍期权的价格.最后,通过数值实例验证了算法的可行性和准确性.     

随机利率下的PPP项目实物期权评价研究

PPP项目通常实施周期长,风险突出。传统的实物期权评价方法考虑了未来的不确定性和管理者柔性的价值,但是一般假设无风险利率是固定的,不符合利率长期内波动的特点,会造成投资者决策失误。本文考虑了未来无风险利率波动条件下,PPP项目中实物期权的价值。首先分析了PPP项目中通常存在的期权形式,其次研究了无风险利率三角逆变函数以及在此基础上得出模拟实物期权模型,并用案例对比分析固定利率和随机利率下的期权价值。结果显示,随机利率比固定利率下的期权价值更高,研究结论可以为PPP项目的投资者进行决策提供重要依据。     

Difference operators,Green's matrix,sampling theory and applications in signal processing

This paper introduces sampling representations for discrete signals arising from self adjoint difference operators with mixed boundary conditions. The theory of linear operators on finite-dimensional inner product spaces is employed to study the second-order difference operators. We give necessary and sufficient conditions that make the operators self adjoint. The equivalence between the difference operator and a Hermitian Green's matrix is established. Sampling theorems are derived for discrete transforms associated with the difference operator. The results are exhibited via illustrative examples, involving sampling representations for the discrete Hartley transform. Families of discrete fractional Fourier-type transforms are introduced with an application to image encryption.     

Bayesian Nonparametric Modeling for Multivariate Ordinal Regression

Univariate or multivariate ordinal responses are often assumed to arise from a latent continuous parametric distribution, with covariate effects that enter linearly. We introduce a Bayesian nonparametric modeling approach for univariate and multivariate ordinal regression, which is based on mixture modeling for the joint distribution of latent responses and covariates. The modeling framework enables highly flexible inference for ordinal regression relationships, avoiding assumptions of linearity or additivity in the covariate effects. In standard parametric ordinal regression models, computational challenges arise from identifiability constraints and estimation of parameters requiring nonstandard inferential techniques. A key feature of the nonparametric model is that it achieves inferential flexibility, while avoiding these difficulties. In particular, we establish full support of the nonparametric mixture model under fixed cut-off points that relate through discretization the latent continuous responses with the ordinal responses. The practical utility of the modeling approach is illustrated through application to two datasets from econometrics, an example involving regression relationships for ozone concentration, and a multirater agreement problem. Supplementary materials with technical details on theoretical results and on computation are available online.     

Sequential Bayesian learning for stochastic volatility with variance‐gamma jumps in returns

In this work, we investigate sequential Bayesian estimation for inference of stochastic volatility with variance‐gamma (SVVG) jumps in returns. We develop an estimation algorithm that combines the sequential learning auxiliary particle filter with the particle learning filter. Simulation evidence and empirical estimation results indicate that this approach is able to filter latent variances, identify latent jumps in returns, and provide sequential learning about the static parameters of SVVG. We demonstrate comparative performance of the sequential algorithm and off‐line Markov Chain Monte Carlo in synthetic and real data applications.     

Asymmetric multi-channel sampling in shift invariant spaces

We develop an asymmetric multi-channel sampling on a shift invariant space V(?) with a Riesz generator ?(t) in L²(R), where each channeled signal is assigned a uniform but distinct sampling rate. We use Fourier duality between V(?) and L²[0,2π] to find conditions under which there is a stable asymmetric multi-channel sampling formula on V(?).