SEMIFARMA-HYGARCH Modeling of Dow Jones Return Persistence

This paper analyzes the cyclical behavior of Dow Jones by testing the existence of long memory through a new class of semiparametric ARFIMA models with HYGARCH errors (SEMIFARMA-HYGARCH); this class includes nonparametric deterministic trend, stochastic trend, short-range and long-range dependence and long memory heteroscedastic errors. We study the daily returns of the Dow Jones from 1896 to 2006. We estimate several models and we find that the coefficients of the SEMIFARMA-HYGARCH model, including long memory coefficients for the equations of the mean and the conditional variance, are highly significant. The forecasting results show that the informational shocks have permanent effects on volatility and the SEMIFARMA-HYGARCH model has better performance over some other models for long and/or short horizons. The predictions from this model are also better than the predictions of the random walk model; accordingly, the weak efficiency assumption of financial markets seems violated for Dow Jones returns studied over a long period.     

Joint segmentation of multivariate Gaussian processes using mixed linear models

The joint segmentation of multiple series is considered. A mixed linear model is used to account for both covariates and correlations between signals. An estimation algorithm based on EM which involves a new dynamic programming strategy for the segmentation step is proposed. The computational efficiency of this procedure is shown and its performance is assessed through simulation experiments. Applications are presented in the field of climatic data analysis.     

Probability method for cryptanalysis of general multivariate modular linear equation

Finding the solution to a general multivariate modular linear equation plays an important role in cryptanalysis field. Earlier results show that obtaining a relatively short solution is possible in polynomial time. However, one problem arises here that if the equation has a short solution in given bounded range, the results outputted by earlier algorithms are often not the ones we are interested in. In this paper, we present a probability method based on lattice basis reduction to solve the problem. For a g...     

Joint order and parameter estimation of multivariate autoregressive models using multi-model partitioning theory

In this paper the multi-model partitioning theory is used for simultaneous order and parameter estimation of multivariate autoregressive models. Simulation experiments show that the proposed method successfully selects the correct model order and estimates the parameters accurately, in very few steps, even with a small sample size. They also show that the proposed method performs equally well when the complexity of the model is increased. The results are compared to those obtained using well-established order selection criteria. Finally, it is shown that the method is also successful in tracking model order changes, in real time.     

代价敏感学习中的损失函数设计

一般的学习算法通过最小化分类损失使分类错误率最小化,而代价敏感学习则以最小化分类代价为目标,需构造代价敏感损失.本文探讨代价敏感损失的设计准则,首先介绍基于代价敏感风险优化的代价敏感学习方法,然后在Bayes最优分类理论框架下,提出两条代价敏感损失设计准则.接着采用两种常用代价敏感损失生成方法构造平方损失、指数损失、对数损失、支持向量机损失等经典损失函数的代价敏感扩展形式.根据所提出的设计准则,从理论上分析这些代价敏感损失的性能.最后通过实验表明,同时满足两条设计准则的代价敏感损失能有效降低分类代价,从而证明了本文提出的代价敏感损失设计准则的合理性.     

Variants of the general interval power function

The article gives an overview of power function variants and compares three differently extensive versions for use in interval arithmetics. Aiming at the general power function for inclusion in interval libraries, which is defined for as many pairs of base and exponent as possible, we observe that the definition of a power for each such pair depends on the context. This problem eventually comes up at the point where reasonable doubts about the definition $0^0$ and powers with negative base in correlation with a non-integral exponent arise. We come up with several variants serving distinct purposes, yet also recommend a general-purpose power function, which is unique for being restricted to integral exponents on the domain of negative bases. Three different variants of interval power functions satisfy all meaningful general exponentiation needs. We provide a unified treatment to handle the variants and present valuable implementation techniques for the computation of these interval functions along with a mathematic foundation.     

Joint modelling of multivariate longitudinal outcomes and a time-to-event: A nonlinear latent class approach

A joint model based on a latent class approach is proposed to explore the association between correlated longitudinal quantitative markers and a time-to-event. A longitudinal latent class model describes latent profiles of evolution of the latent process underlying the correlated markers. The latent process is linked to the markers by nonlinear transformations including parameters to be estimated. A proportional hazard model describes the joint risk of event according to the latent classes and two specifications of the risk function are considered: a parametric function and a semi-parametric function based on splines. Depending on the chosen risk function, estimation is performed by a maximum likelihood or a maximum penalized likelihood approach. A simulation study validates the estimation procedure. As a latent class model relies on the strong assumption that the markers and the time-to-event are independent conditionally on the latent classes, a test of conditional independence is proposed using the residuals conditional on time-to-event. The procedure does not require any posterior classification and can be conducted using standard statistical softwares. The methodology is applied to describe profiles of cognitive decline in the elderly and their associated risk of dementia.     

信道非对称下的联合预编码方案

在多基站(BS)协作系统中,信道的非对称导致相互协作的两基站从协作中获得的性能增益不一样。因此,在选择协作基站的过程中,当一方想合作,而另一方更想与其他基站合作的时候,分簇问题变得非常困难,使整个系统性能受限。为解决此问题,定义了协同度的概念,针对此情况提出了一种重叠分簇方案,并在此基础上设计了一种联合迫零-汤姆林森-哈拉希玛预编码（ZF-THP）算法。该方案通过对局部基站进行重叠分簇调整,并利用THP技术消除重叠基站带来的干扰。仿真分析表明,该方案较好地解决了协同度非对称性导致的分簇矛盾,提高了系统的频谱效率,并改善了用户的公平性能。     

To the optimal identification of multivariate systems under perturbations of unknown covariances

We consider the problem of parameter and covariance estimation for multivariate stochastic systems described by regression models with special structure perturbations of unknown covariance. Sufficient conditions of uniformly optimal estimations are obtained for system parameters and covariances. The observation vector distribution family is factorized, and the full sufficient statistics is found under those conditions. Equations for uniformely optimal unbiased estimates of covariance parameters are obtained.     

Analytic and bootstrap approximations of prediction errors under a multivariate Fay-Herriot model

The prediction of vectors of small area quantities based on a multivariate Fay-Herriot model is addressed. For this, an empirical best linear unbiased predictor (EBLUP) of the target vector is used, where the model parameters are estimated by two different methods based on moments. The mean cross product error matrix of the multidimensional EBLUP is approximated both analytically and by a wild bootstrap method that yields direct and bias-corrected bootstrap estimators. A simulation study compares the small sample properties of the bootstrap estimators and the analytical approximation, including a comparison under lack of normality. Finally, the number of replicates needed for the bootstrap procedures to get stabilized are studied.     

MULCOX2: a general computer program for the Cox regression analysis of multivariate failure time data

Multivariate failure time data is commonly encountered in biomedicine, because each study subject may experience multiple events or because there exists clustering of subjects such that failure times within the same cluster are correlated. MULCOX2 implements a general statistical methodology for analyzing such data. This approach formulates the marginal distributions of multivariate failure times by Cox proportional hazards models without specifying the nature of dependence among related failure times. The baseline hazard functions for the marginal models may be identical or different. A variety of statistical inference can be made regarding the effects of (possibly time-dependent) covariates on the failure rates. Although designed primarily for the marginal approach, MULCOX2 is general enough to implement several alternative methods. The program runs on any computer with a FORTRAN compiler. The running time is minimal. Two illustrative examples are provided.     

Linux下通用线程池的改进与实现

对线程池的阻塞唤醒机制,动态调整,线程安全退出,参数处理,系统线程数限制等细节进行研究,保证了其在不同应用场景下的独立性和通用性;同时采用一种基于数组的链表机制来改进线程池的查找分配算法,将其时间复杂度稳定在O（1）,避免了传统线程池当线程数目过大时导致的查询分配性能下降的问题。实验结果表明,改进后的线程池与传统的系统线程分配方式相比在开销上有很大节省。     

Interpretable tourism volume forecasting with multivariate time series under the impact of COVID-19

Neural Computing and Applications - This study proposes a novel interpretable framework to forecast the daily tourism volume of Jiuzhaigou Valley, Huangshan Mountain, and Siguniang Mountain in...     

Conquering inseparability: Primary decomposition and multivariate factorization over algebraic function fields of positive characteristic

Algebraic function fields of positive characteristic are non-perfect fields, and many standard algorithms for solving some fundamental problems in commutative algebra simply do not work over these fields. This paper presents practical algorithms for the first time for (1) computing the primary decomposition of ideals of polynomial rings defined over such fields and (2) factoring arbitrary multivariate polynomials over such fields. Difficulties involving inseparability and the situation where the transcendence degree is greater than one are completely overcome, while the algorithms avoid explicit construction of any extension of the input base field. As a corollary, the problem of computing the primary decomposition of a positive-dimensional ideal over a finite field is also solved. The algorithms perform very effectively in an implementation within the Magma Computer Algebra System, and an analysis of their practical performance is given.     

A power function model for determining sample sizes for the operations of multivariate control charts

Using the hypothesis-testing approach, we develop a model for determining sample sizes for the operation of multivariate control charts. A simple solution procedure that can be processed on any personal or small computer is also developed. The effect of correlation between pairs of variables on the performance of the model is studied. The performances of multivariate and univariate control charts are compared under the model. Before the development of the model, a brief review of multivariate test of hypothesis and multivariate control charts was done. The model is recommended for any quality control engineer who may like to specify a desired level of protection against inferior quality.     

双循环策略下岸桥与跨运车的联合调度

集装箱码头采用跨运车能够减少作业环节和码头机械设备的种类与数量,同时缓存区容量的设置至关重要。首先,为降低码头总完工时间、提高码头作业效率,并解决采用跨运车作为水平运输设备与岸桥进行联合装卸作业时产生的时空协调问题,引入了双循环操作策略,对岸桥与跨运车的联合作业序列优化问题进行研究。其次,建立以总完工时间最小化为目标,考虑岸桥与跨运车双循环操作的实际约束、岸桥缓存区容量限制、安全时间等约束的混合整数规划模型。然后,针对传统禁忌搜索（TS）算法的局限性,加入贪婪算法、多种邻域搜索方式、响应性策略,设计了基于贪婪算法的响应性TS算法,并进行了数值实验。实验结果验证了所提模型与算法的有效性。最后,通过对缓存区容量与跨运车数量、岸桥与跨运车配比的实验分析,得出了最优的跨运车数量和缓存区容量、岸桥与跨运车配比。结果表明：与传统码头设备配置相比,双循环策略可减少跨运车使用数量,提高岸桥与跨运车使用率。