ON THE UNIMODALITY OF THE EXACT LIKELIHOOD FUNCTION FOR NORMAL AR(2) SERIES

Abstract. For stationary second-order autoregressive normal processes, the conjecture of uniqueness of the solution of the exact likelihood equations is examined. A sufficient condition for uniqueness is given; this condition is satisfied with very high probability if the number of observations is not extremely small. Moreover, it is shown that not more than two maxima may exist. Examples of data which actually produce a likelihood function with two local maxima are given.     

SOME PROPERTIES OF THE MAXIMUM LIKELIHOOD ESTIMATOR IN THE SIMULTANEOUS SWITCHING AUTOREGRESSIVE MODEL

Abstract. The simultaneous switching autoregressive (SSAR) model proposed by Kunitomo and Sato (A non-linearity in economic time series and disequilibrium econometric models. In Theory and Application of Mathematical Statistics (ed. A. Takemura). Tokyo:University of Tokyo Press (in Japanese), 1994; Asymmetry in economic time series and simultaneous switching autoregressive model. Struct. Change Econ. Dyn. , forthcoming (1994).) is a Markovian non-linear time series model. We investigate the finite sample as well as the asymptotic properties of the least squares estimator and the maximum likelihood (ML) estimator. Due to a specific simultaneity involved in the SSAR model, the least squares estimator is badly biased. However, the ML estimator under the assumption of Gaussian disturbances gives reasonable estimates.     

Maximum principle for mean-field jump–diffusion stochastic delay differential equations and its application to finance

This paper investigates a stochastic optimal control problem with delay and of mean-field type, where the controlled state process is governed by a mean-field jump–diffusion stochastic delay differential equation. Two sufficient maximum principles and one necessary maximum principle are established for the underlying system. As an application, a bicriteria mean–variance portfolio selection problem with delay is studied to demonstrate the effectiveness and potential of the proposed techniques. Under certain conditions, explicit expressions are provided for the efficient portfolio and the efficient frontier, which are as elegant as those in the classical mean–variance problem without delays.     

基于新型混合模型的欠定盲分离方法

针对欠定盲分离问题,提出了一种新的源恢复方法. 在时频域局部区域采用复高斯分布对源信号进行建模,将语音信号的稀疏性和局部平稳性结合在一起,提出了一种新的混合模型来描述观测信号在局部区域的概率分布.通过该模型,将每个时频点的源信号状态的判断问题转换成模型的参数估计和后验概率的计算问题,最后通过子混合矩阵的逆恢复出源信号. 实验结果表明,该方法具有很快的收敛速度,并且比已有方法具有更好的分离性能.     

An understanding of high entropy alloys from phase diagram calculations

The concept of High Entropy Alloy (HEA) is understood from the point of view of phase diagram calculation. The role of entropy of mixing on the phase stability is discussed for both ideal and non-ideal solid solution phases. The relative stability of a solid solution phase and line compounds is illustrated using hypothetical systems. Calculated binary and multicomponent phase diagrams are used to explain the phenomena observed experimentally for HEAs. The potential of using the CALPHAD (CALculation of PHAse Diagrams) approach in aiding the design of alloys with multiple key components is also discussed.     

淮河流域水资源短缺风险评估与时空分析

根据2003-2013年淮河流域五省的降水量、径流系数、人均水资源量、人均GDP、人口密度等相关数据,构建水资源短缺风险评价体系,用熵权法对指标赋值,运用可变模糊模型对淮河流域及各省的水资源短缺风险进行评估和时空差异分析。结果表明:2003-2013年间淮河流域水资源短缺风险值总体较高,且呈缓慢的增长趋势,2004年降水较少,风险值达到最高;十年间河南省风险增加最为明显,而山东省较为稳定,风险增加也最低;在淮河流域五个区域中,河南省的风险程度最高,达到3.52;江苏省、山东省次之;安徽省和湖北省相对较低,达到2.86和2.51。水资源短缺风险二级指标分析发现,危险性最强的是河南省,安徽省最小;水资源短缺易损性最强的是山东省,湖北省最小;水资源短缺暴露性最强的是江苏省,山东省最小;水资源短缺可恢复性最好的是湖北省,河南省最差。同时,所有评价指标中人口密度、人均GDP、降水量对水资源短缺风险的影响较大。     

基于DPSIR和主成分分析的阜康市水资源承载力评价

针对新疆自治区阜康市水资源日益短缺及生态环境逐渐恶化的现实情况,根据DPSIR框架展开水资源承载力因子分析,构建了水资源承载力多因素基础指标体系;应用主成分分析法筛选出主要指标,采用熵权法进行赋权;构建综合评价模型对水资源承载力进行计算。研究表明:筛选出的4个主成分累计贡献率为92.016%,能充分代表多因素基础指标体系;阜康市九年间(2006年-2014年)水资源承载力综合评分值在0.498~0.531之间波动,下降速率缓慢且波动幅度减小,整体上处于开发饱和阶段。社会和经济发展的压力带来的农业用水的增加是导致水资源承载力下降的主要指标,扩大地下水开采和推行的节水灌溉等压采措施则在很大程度上缓解了下降的趋势。     

Personalized identification of abdominal wall hernia meshes on computed tomography

An abdominal wall hernia is a protrusion of the intestine through an opening or area of weakness in the abdominal wall. Correct pre-operative identification of abdominal wall hernia meshes could help surgeons adjust the surgical plan to meet the expected difficulty and morbidity of operating through or removing the previous mesh. First, we present herein for the first time the application of image analysis for automated identification of hernia meshes. Second, we discuss the novel development of a new entropy-based image texture feature using geostatistics and indicator kriging. Third, we seek to enhance the hernia mesh identification by combining the new texture feature with the gray-level co-occurrence matrix feature of the image. The two features can characterize complementary information of anatomic details of the abdominal hernia wall and its mesh on computed tomography. Experimental results have demonstrated the effectiveness of the proposed study. The new computational tool has potential for personalized mesh identification which can assist surgeons in the diagnosis and repair of complex abdominal wall hernias.     

A RANDOM PARAMETER PROCESS FOR MODELING AND FORECASTING TIME SERIES

Abstract. A generalized autoregressive (GAR) process {Z ( t ) ; t = 0 , ±1, …} is defined to satisfy the recurrence relation Z(t) = A_θ (t)Z (t -l)+ u( t ), where {A_θ(t); t = 0,±1, …} is itself a stochastic process depending on a vector parameter θ and where {u( t ); t = 0, ±1, …} is white noise with Eu ² ( t ) = a ². This paper develops theory and methodology and implementing the class of GAR processes for time series modeling and forecasting. Conditions on the 'parameter process' { A _θ ( t ); t = 0, ±1, …} are obtained for the existence of a GAR process; necessary and sufficient conditions on { A_θ ( t ) ; t = 0, ±1, …} for existence of a stationary GAR process are also obtained. Procedures are developed for computing maximum likelihood estimates of the parameters 0 and u² and for computing the minimum mean squared error forecasts for GAR processes.