Maximal and total skew information for a two-qubit system using nonlinear interaction models

Maximal and total skew information is studied. For symmetric pure states of two-qubit, they are closely related to the linear entropy, the concurrence, and the spin squeezing parameter. For a two-qubit system implemented in three nonlinear interaction models with an external field, we give the exact state vectors and the expectation value （Sz） at any time t. Based on （Sz）2, we give the maximal and the total skew information and a condition in which the maximal and the total skew information can reach 1 and 2, respectively.

A parametric bootstrap approach for one-way classification model with skew-normal random effects

In this paper,several properties of one-way classification model with skew-normal random effects are obtained,such as moment generating function,density function and noncentral skew chi-square distribution,etc.Based on the EM algorithm,we discuss the maximum likelihood(ML)estimation of unknown parameters.For testing problem of fixed effect,a parametric bootstrap(PB)approach is developed.Finally,some simulation results on the Type I error rates and powers of the PB approach are obtained,which show that the PB approach provides satisfactory performances on the Type I error rates and powers,even for small samples.For illustration,our main results are applied to a real data problem.

Maximal and total skew information of three-qubit system obtained using nonlinear interaction models

Both the maximal and the total skew information have been studied. For a three-qubit system implemented in three nonlinear interaction models, we give the exact state vector at any time t. Beused on this, we give the maximal and the total skew information. It is found that they have the same form and their evolution periods are dependent on the energy difference between the ground state and the second excited state in these models. The maximal skew information is always in the （Sx, Sv） plane. We give the condition for the occurrence of IGHZ}sy, in which they can reach the extreme values of 9/4 and 15/4, respectively. In three different decoherence channels, two kinds of information and the concurrence are calculated. We find that the phenomenon of the concurrence of sudden death occurs, but the above two kinds of information do not die suddenly. In the phase-damping channel, the two kinds of information will not be lost completely.

A skew–normal mixture of joint location, scale and skewness models

Normal mixture regression models are one of the most important statistical data analysis tools in a heterogeneous population. When the data set under consideration involves asymmetric outcomes, in the last two decades, the skew normal distribution has been shown beneficial in dealing with asymmetric data in various theoretic and applied problems. In this paper, we propose and study a novel class of models： a skew-normal mixture of joint location, scale and skewness models to analyze the heteroscedastic skew-normal data coming from a heterogeneous population. The issues of maximum likelihood estimation are addressed. In particular, an Expectation-Maximization （EM） algorithm for estimating the model parameters is developed. Properties of the estimators of the regression coefficients are evaluated through Monte Carlo experiments. Results from the analysis of a real data set from the Body Mass Index （BMI） data are presented.

Joint modelling of location and scale parameters of the skew-normal distribution

Joint location and scale models of the skew-normal distribution provide useful ex- tension for joint mean and variance models of the normal distribution when the data set under consideration involves asymmetric outcomes. This paper focuses on the maximum likelihood estimation of joint location and scale models of the skew-normal distribution. The proposed procedure can simultaneously estimate parameters in the location model and the scale model. Simulation studies and a real example are used to illustrate the proposed methodologies.

Variable Selection for Robust Mixture Regression Model with Skew Scale Mixtures of Normal Distributions

In this paper, we propose a robust mixture regression model based on the skew scale mixtures of normal distributions (RMR-SSMN) which can accommodate asymmetric, heavy-tailed and contaminated data better. For the variable selection problem, the penalized likelihood approach with a new combined penalty function which balances the SCAD and l2 penalty is proposed. The adjusted EM algorithm is presented to get parameter estimates of RMR-SSMN models at a faster convergence rate. As simulations show, our mixture models are more robust than general FMR models and the new combined penalty function outperforms SCAD for variable selection. Finally, the proposed methodology and algorithm are applied to a real data set and achieve reasonable results.

基于Skewed-t分布的FIGARCH模型与VaR的度量

金融资产收益率序列的波动具有典型的尖峰厚尾和非对称性特征,描述这种特性需以合适的概率分布函数为基础.因此,寻求更好的概率分布函数对风险度量、VaR的计算有着十分重要的意义.有鉴于此引入Skewed-t分布度量VaR,并比较分析了RiskMetrics及FIGARCH类模型度量VaR值的准确程度,本文同时分析了多头头寸和空头头寸情况下的VaR.结果表明,在两种头寸情况下,Skewed-t分布在空头和多头情形对资产厚尾特性以及非对称性的拟合效果均要比正态分布好;在两种头寸中不同的置信水平下,FIAGARCH(CHUNG)模型预测的VaR值改进了使用传统模型的精确性,高估或低估风险的程度较轻.

农产品期货价格波动特征分析——基于Beta-skew-t-EGARCH模型的实证分析

根据2008~2019年国内大豆、玉米、棉花、豆粕和白糖期货合约日收盘价格数据,通过建立基于Beta-skew-t-EGARCH模型研究了农产品期货价格波动规律,实证研究结果表明:农产品期货合约价格波动具有较强的持续性和聚集性且呈现出杠杆效应,并且在履约期前后价格剧烈波动。以玉米为例,从供求关系和物价指数2个方面分析了波动成因以及影响程度。从供给层面角度来看,种植面积的上升从源头推动了玉米产量的上涨,从而稳定了玉米价格;从需求层面角度来看,人口总数和人均GDP的增加扩大了玉米的需求量,促使了玉米价格持续上涨。因此,建议我国保持农产品种植面积的平稳,稳定农产品供给,同时,还要提高农产品价格市场监测和预警能力,并积极优化期货交易主体结构。

摩擦市场条件下的Mean-Variance-Skewness模型

在投资组合选择模型中考虑了资产收益率分布中正的偏度水平,并通过引入一些市场摩擦因素建立了摩擦市场条件下的Mean-Variance-Skewness模型.提出了一个新的遗传算法加速其搜索收敛过程,解决了该模型的计算复杂性问题.在该模型框架内对交易费用和税收等市场摩擦因素进行了敏感性分析.研究证明资产收益率分布的偏度水平是与投资者的决策相关的,市场摩擦因素对投资者的决策行为也有直接的影响.因此,考虑摩擦市场条件下基于正偏度水平偏好的最优投资组合模型对投资者有很强的实践指导价值.

Performance-based seismic assessment of skewed bridges with and without considering soil-foundation interaction effects for various site classes

The seismic behavior of skewed bridges has not been well studied compared to straight bridges. Skewed bridges have shown extensive damage, especially due to deck rotation, shear keys failure, abutment unseating and column- bent drift. This research, therefore, aims to study the behavior of skewed and straight highway overpass bridges both with and without taking into account the effects of Soil-Structure Interaction （SSI） due to near-fault ground motions. Due to several sources of uncertainty associated with the ground motions, soil and structure, a probabilistic approach is needed. Thus, a probabilistic methodology similar to the one developed by the Pacific Earthquake Engineering Research Center （PEER） has been utilized to assess the probability of damage due to various levels of shaking using appropriate intensity measures with minimum dispersions. The probabilistic analyses were performed for various bridge configurations and site conditions, including sand ranging from loose to dense and clay ranging from soft to stiff, in order to evaluate the effects. The results proved a considerable susceptibility of skewed bridges to deck rotation and shear keys displacement. It was also found that SSI had a decreasing effect on the damage probability for various demands compared to the fixed-base model without including SSI. However, deck rotation for all types of the soil and also abutment unseating for very loose sand and soft clay showed an increase in damage probability compared to the fixed-base model. The damage probability for various demands has also been found to decrease with an increase of soil strength for both sandy and clayey sites. With respect to the variations in the skew angle, an increase in skew angle has had an increasing effect on the amplitude of the seismic response for various demands. Deck rotation has been very sensitive to the increase in the skew angle; therefore, as the skew angle increased, the deck rotation responded accordingly. Furthermore, abutment unseating showed an increasing trend due to an increase in skew angle for both fixed-base and SSI models.

Seismic response of skewed RC box-girder bridges

It is critical to ensure the functionality of highway bridges after earthquakes to provide access to important facilities. Since the 1971 San Fernando earthquake, there has been a better understanding of the seismic performance of bridges. Nonetheless, there are no detailed guidelines addressing the performance of skewed highway bridges. Several parameters affect the response of skewed highway bridges under both service and seismic loads which makes their behavior complex. Therefore, there is a need for more research to study the effect of skew angle and other related factors on the performance of highway bridges. This paper examines the seismic performance of a three-span continuous concrete box girder bridge with skew angles from 0 to 60 degrees, analytically. Finite element （FE） and simplified beam-stick （BS） models of the bridge were developed using SAP2000. Different types of analysis were considered on both models such as： nonlinear static pushover, and linear and nonlinear time history analyses. A comparison was conducted between FE and BS, different skew angles, abutment support conditions, and time history and pushover analysis. It is shown that the BS model has the capability to capture the coupling due to skew and the significant modes for moderate skew angles. Boundary conditions and pushover load profile are determined to have a major effect on pushover analysis. Pushover analysis may be used to predict the maximum deformation and hinge formation adequately.

A numerical study for boundary layer current and sheet flow transport induced by a skewed asymmetric wave

An analytical model with essential parameters given by a two-phase numerical model is utilized to study the net boundary layer current and sediment transport under skewed asymmetric oscillatory sheet flows. The analytical model is the first instantaneous type model that can consider phase-lag and asymmetric boundary layer development. The two-phase model supplies the essential phase-lead, instantaneous erosion depth and boundary layer development for the analytical model to enhance the understanding of velocity skewness and acceleration skewness in sediment flux and transport rate. The sediment transport difference between onshore and offshore stages caused by velocity skewness or acceleration skewness is shown to illustrate the determination of net sediment transport by the analytical model. In previous studies about sediment transport in skewed asymmetric sheet flows, the generation of net sediment transport is mainly concluded to the phase-lag effect.However, the phase-lag effect is shown important but not enough for the net sediment transport, while the skewed asymmetric boundary layer development generated net boundary layer current and mobile bed effect are key important in the transport process.

The role of velocity derivative skewness in understanding non-equilibrium turbulence

The turbulence governed by the Navier-Stokes equation is paramount in many physical processes.However,it has been considered as a challenging problem due to its inherent nonlinearity,non-equilibrium,and complexity.Herein,we review the connections between the velocity derivative skewness Sk and the non-equilibrium properties of turbulence.Sk,a reasonable candidate for describing the non-equilibrium turbulence,which varies during the non-equilibrium procedure.A lot of experimental or numerical evidences have shown that the perturbation of energy spectrum,which associated with the excitation of large scales,results in an obvious variation of Sk,and Sk is a negative value in this rapid energy decay process.The variation of positive Sk is closely related to the perturbation of transfer spectrum,and this corresponds to the backward energy transfer process.In addition,the skewness characterizes the production(or reduction)rate of enstrophy due to vortex stretching(or compression).Using the transport equation of turbulent energy dissipation rate and enstrophy,it is possible to establish a theoretical connection between skewness and the non-equilibrium turbulence.It is expected that this work could trigger the rapid advancement of the future studies of non-equilibrium turbulence,and also the improvement of turbulence models.

Dose-Injury Relation as a Model for Uncertainty Propagation from Input Dose to Target Dose

We study a general framework for assessing the injury probability corresponding to an input dose quantity. In many applications, the true value of input dose may not be directly measurable. Instead, the input dose is estimated from measurable/controllable quantities via numerical simulations using assumed representative parameter values. We aim at developing a simple modeling framework for accommodating all uncertainties, including the discrepancy between the estimated input dose and the true input dose. We first interpret the widely used logistic dose-injury model as the result of dose propagation uncertainty from input dose to target dose at the active site for injury where the binary outcome is completely determined by the target dose. We specify the symmetric logistic dose-injury function using two shape parameters: the median injury dose and the 10 - 90 percentile width. We relate the two shape parameters of injury function to the mean and standard deviation of the dose propagation uncertainty. We find 1) a larger total uncertainty will spread more the dose-response function, increasing the 10 - 90 percentile width and 2) a systematic over-estimate of the input dose will shift the injury probability toward the right along the estimated input dose. This framework provides a way of revising an established injury model for a particular test population to predict the injury model for a new population with different distributions of parameters that affect the dose propagation and dose estimation. In addition to modeling dose propagation uncertainty, we propose a new 3-parameter model to include the skewness of injury function. The proposed 3-parameter function form is based on shifted log-normal distribution of dose propagation uncertainty and is approximately invariant when other uncertainties are added. The proposed 3-parameter function form provides a framework for extending skewed injury model from a test population to a target population in application.

Pricing VIX options in a 3/2 plus jumps model

This paper proposes and makes a study of a new model(called the 3/2 plus jumps model) for VIX option pricing. The model allows the mean-reversion speed and volatility of volatility to be highly sensitive to the actual level of VIX. In particular, the positive volatility skew is addressed by the 3/2 plus jumps model. Daily calibration is used to prove that the proposed model preserves its validity and reliability for both in-sample and out-of-sample tests.The results show that the models are capable of fitting the market price while generating positive volatility skew.

The Importance of Computational Grids in Hydraulic Numerical Models

The importance of computational grids in hydraulic numerical models is studied by numerical simulation of jet flow in a rectangular duct which is linked with a fixed width inlet and a different width outlet using a standard k-epsilon turbulence model. The computational results show the numerical solutions may not be reasonable because of the incorrect computational grid and each numerical model bass grid-independent solution. The computational grid has a definitive effect on the accuracy and stability of the computational solution, which must be divided well according to the simulated geometry and physical characters of hydraulic problems. The main guidelines about the formation of computational grid in such aspects as node distribution, smoothness and skewness of grid, have been given.

Simulation Program to Determine Sample Size and Power for a Multiple Logistic Regression Model with Unspecified Covariate Distributions

Binary logistic regression models are commonly used to assess the association between outcomes and covariates. Many covariates are inherently continuous, and have a variety of distributions, including those that are heavily skewed to the left or right. Existing theoretical formulas, criteria, and simulation programs cannot accurately estimate the sample size and power of non-standard distributions. Therefore, we have developed a simulation program that uses Monte Carlo methods to estimate the exact power of a binary logistic regression model. This power calculation can be used for distributions of any shape and covariates of any type (continuous, ordinal, and nominal), and can account for nonlinear relationships between covariates and outcomes. For illustrative purposes, this simulation program is applied to real data obtained from a study on the influence of smoking on 90-day outcomes after acute atherothrombotic stroke. Our program is applicable to all effect sizes and makes it possible to apply various statistical methods, logistic regression and related simulations such as Bayesian inference with some modifications.

Attenuate Class Imbalance Problem for Pneumonia Diagnosis Using Ensemble Parallel Stacked Pre-Trained Models

Pneumonia is an acute lung infection that has caused many fatalitiesglobally. Radiologists often employ chest X-rays to identify pneumoniasince they are presently the most effective imaging method for this purpose.Computer-aided diagnosis of pneumonia using deep learning techniques iswidely used due to its effectiveness and performance. In the proposed method,the Synthetic Minority Oversampling Technique (SMOTE) approach is usedto eliminate the class imbalance in the X-ray dataset. To compensate forthe paucity of accessible data, pre-trained transfer learning is used, and anensemble Convolutional Neural Network (CNN) model is developed. Theensemble model consists of all possible combinations of the MobileNetv2,Visual Geometry Group (VGG16), and DenseNet169 models. MobileNetV2and DenseNet169 performed well in the Single classifier model, with anaccuracy of 94%, while the ensemble model (MobileNetV2+DenseNet169)achieved an accuracy of 96.9%. Using the data synchronous parallel modelin Distributed Tensorflow, the training process accelerated performance by98.6% and outperformed other conventional approaches.

生猪价格波动特征分析——基于Beta-skew-t-EGARCH模型的实证分析

生猪产业收入是我国农业经济的主要收入,生猪价格的波动不仅牵动着生猪产业众多参与者的利益,对整个农业经济的健康发展和人民群众的幸福生活皆有重大影响。文章基于2017-2020年江苏、湖北和四川生猪现货价格数据,采用Beta-skew-t-EGARCH模型研究生猪价格波动规律及特征,结果表明:生猪现货价格波动具有较强的持续性和聚集性,且呈现出杠杆效应,在每年一季度价格剧烈波动。因此,建议生猪养殖向规模化发展,尽早推出相关期货品种以丰富和稳定生猪市场,加强对生猪价格市场监测和预警。

Bayesian Inference of Spatially Correlated Binary Data Using Skew-Normal Latent Variables with Application in Tooth Caries Analysis

The analysis of spatially correlated binary data observed on lattices is an interesting topic that catches the attention of many scholars of different scientific fields like epidemiology, medicine, agriculture, biology, geology and geography. To overcome the encountered difficulties upon fitting the autologistic regression model to analyze such data via Bayesian and/or Markov chain Monte Carlo (MCMC) techniques, the Gaussian latent variable model has been enrolled in the methodology. Assuming a normal distribution for the latent random variable may not be realistic and wrong, normal assumptions might cause bias in parameter estimates and affect the accuracy of results and inferences. Thus, it entails more flexible prior distributions for the latent variable in the spatial models. A review of the recent literature in spatial statistics shows that there is an increasing tendency in presenting models that are involving skew distributions, especially skew-normal ones. In this study, a skew-normal latent variable modeling was developed in Bayesian analysis of the spatially correlated binary data that were acquired on uncorrelated lattices. The proposed methodology was applied in inspecting spatial dependency and related factors of tooth caries occurrences in a sample of students of Yasuj University of Medical Sciences, Yasuj, Iran. The results indicated that the skew-normal latent variable model had validity and it made a decent criterion that fitted caries data.