Measures of dependence for the multivariate t distribution with applications to the stock market

Two measures of dependence for multivariate t and Cauchy random variables are developed based on Kullback–Leibler number. The mutual information number T(X) is obtained in a closed expression form, as well as its asymptotic distribution. A dependence coefficient ρ₁, is defined (based on the Kullback–Leibler number) with the properties of ρ₁=0 indicating independence and ρ₁=1indicating degeneracy. Two real life examples from the stock market are used to analyze the level of dependence and correlation among stocks.     

Estimation of signal to noise ratio using mahalanobis distance

In this paper, we propose some alternative estimatiors to that given by C. G. Khatri and C. R. Rao (1985), for estimating Signal to Noise ratio. Using Pitman Nearness, Condition for prefering one estimator over the other is estabilished. It is shown numerically that estimators corresponding to Entropy loss function are better more oftern than those corresponding to Squared Error loss.     

Confidence Intervals for Gini's Diversity Measure and Shannon's Entropy Using Adjusted Proportions

Abstract

Asymptotic confidence intervals are given for two functions of multinomial outcome probabilities: Gini's diversity measure and Shannon's entropy. “Adjusted” proportions are used in all asymptotic mean and variance formulas, along with a possible logarithmic transformation. Exact confidence coefficients are computed in some cases. Monte Carlo simulation is used in other cases to compare actual coverages to nominal ones. Some recommendations are made.     

算法隐含并行性的物理模型

利用物理学原理对算法的隐含并行性进行了分析,提出算法的不确定性和高熵态是隐含并行性出现的根源,但算法的隐含并行性会导致算法结果的不确定性。智能算法中先验知识确定程序的搜索方向,隐含并行性提供了对解空间的高速并行搜索,为研究和设计智能算法提供了理论基础。提高算法的先验信息量和隐含并行能力可以得到具备较高智能水平的算法。     

基于AHP法和熵权法的独立学院教师绩效考核

文章针对独立学院教师绩效考核过程中存在指标体系不健全、考核成绩计算过程中各项目权重分配不科学等问题,提出将基于层次分析法的主观赋权法与基于熵的客观赋权法相结合的主客观赋权法,以此提高权重分配的科学性与准确性;最后,选取某独立学院的教师进行实证分析.     

蛋白质结构与密码子上下文关联的信息论研究

从信息学的角度，对人(Homo sapiens)的117个蛋白质和大肠杆菌(Ecoli)的185个蛋白质的各二级结构相对应的mRNA序列中的密码子上下文关联、蛋白质序列中氨基酸上下文关联作了统计分析，计算了各二级结构中密码子(氨基酸)上下文关联的信息剩余．结果显示，同义密码子关联有着不同于氨基酸关联偏好的反常现象，人和大肠杆菌蛋白质中反常的密码子分别占19％、16％左右；并对大肠杆菌中的稀有密码子的关联进行了讨论，发现稀有密码子的关联偏好Sheet和Coil二级结构．     

Renyi's entropy for residual lifetime distribution

In the present paper, we introduce and study Renyi's information measure (entropy) for residual lifetime distributions. It is shown that the proposed measure uniquely determines the distribution. We present characterizations for some lifetime models. Further, we define two new classes of life distributions based on this measure. Various properties of these classes are also given.     

Applied state space modelling of non-Gaussian time series using integration-based Kalman filtering

The main topic of the paper is on-line filtering for non-Gaussian dynamic (state space) models by approximate computation of the first two posterior moments using efficient numerical integration. Based on approximating the prior of the state vector by a normal density, we prove that the posterior moments of the state vector are related to the posterior moments of the linear predictor in a simple way. For the linear predictor Gauss-Hermite integration is carried out with automatic reparametrization based on an approximate posterior mode filter. We illustrate how further topics in applied state space modelling, such as estimating hyperparameters, computing model likelihoods and predictive residuals, are managed by integration-based Kalman-filtering. The methodology derived in the paper is applied to on-line monitoring of ecological time series and filtering for small count data.     

中国数字经济发展综合评价与预测

文章结合数字经济的内涵和外延,分别从数字基础设施、数字产业化、产业数字化等方面构建了数字经济发展水平量化指标体系,基于熵值法测度了中国2010—2018年数字经济发展趋势,并建立灰色预测模型对2019—2028年的数字经济发展走向进行预测。研究结果表明:2010—2018年,中国数字经济发展水平增长了4.4倍,年均增速达到50%,2019—2028年,中国数字经济发展水平增速将不断提高,预计2028年数字经济发展水平将是2018年的5倍。因此,我国要进一步推动新型数字经济基础设施建设,加快传统产业的数字化转型,积极发展电信业等数字产业,以保障我国数字经济快速发展。     

耗散结构视角下的矿业城市产业转型路径

针对矿业城市产业结构现状,以耗散结构理论为依据,对政府政策、投资环境、中小企业、技术创新、人才培训等环节在产业转型中的作用进行了分析,阐述了产业耗散结构的形成机制。