Information theory of optimization in chromatography: Analytical structure of optimization

Summary The analytical roles of chromatographic variables (column length, etc.) can be soundly comprehended and compared in terms of the precision (Φ) of measurements and efficiency (ϑ) of analysis which are described as Shannon information and information flow, respectively. The φϑ plots of the optimization process and the information Φ transmitted by a single peak are useful to understand the analytical structure of optimization. Variables treated here are mobile phase composition (X), column length (L), mobile phase velocity rate (u), detection wavelength (λ) and plate number (N).     

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We extend the instrumental variable method for the mean regression models to linear quantile regression models with errors-in-variables. The proposed estimator is consistent and asymptotically normally distributed under some fairly general conditions. Moreover, this approach is practical and easy to implement. Simulation studies show that the finite sample performance of the estimator is satisfactory. The method is applied to a real data study of education and wages.     

Hierarchical Archimedean copulas through multivariate compound distributions

In this paper, we propose a new hierarchical Archimedean copula construction based on multivariate compound distributions. This new imbrication technique is derived via the construction of a multivariate exponential mixture distribution through compounding. The absence of nesting and marginal conditions, contrarily to the nested Archimedean copulas approach, leads to major advantages, such as a flexible range of possible combinations in the choice of distributions, the existence of explicit formulas for the distribution of the sum, and computational ease in high dimensions. A balance between flexibility and parsimony is targeted. After presenting the construction technique, properties of the proposed copulas are investigated and illustrative examples are given. A detailed comparison with other construction methodologies of hierarchical Archimedean copulas is provided. Risk aggregation under this newly proposed dependence structure is also examined.     

Fundamental solution of the time‐fractional telegraph Dirac operator

In this work, we obtain the fundamental solution (FS) of the multidimensional time‐fractional telegraph Dirac operator where the 2 time‐fractional derivatives of orders α∈]0,1] and β∈]1,2] are in the Caputo sense. Explicit integral and series representation of the FS are obtained for any dimension. We present and discuss some plots of the FS for some particular values of the dimension and of the fractional parameters α and β. Finally, using the FS, we study some Poisson and Cauchy problems.     

变量有界半定最小二乘问题

<正>1引言设A_i∈S~n,i=1,…,m,定义线性算子A:S~n→R~m,AX=(A_1·X,…,A_m·X)~T,其相应的伴随算子为A~*:R~m→S~n,且A~*y=sum from i=1 to my_iA_i.X∈S~n,b∈R~m.Malick.J在[6]中讨论了如下标准半定最小二乘问题(SDLS):     

NA随机变量序列的Egorov型强大数律的等价条件

本文研究了NA随机变量的Egorov型强大数律.利用NA随机变量的概率不等式,得到了NA随机变量序列的Egorov型强大数律的一些等价条件,所获结果推广和改进了在独立随机变量序列的Egorov的结果和在NA随机变量序列已有的一些结果.     

ND阵列加权乘积和的完全收敛性

设（Xni：1≤i≤n,n≥1）为行间ND阵列,g（x）是R^＋上指数为α的正则变化函数,{αni：1≤i≤n,n≥1}为满足条件max1≤i≤n｜ani｜=0（（g（n））^-1）的实数阵列．本文采用截尾的方法,得到了使ND随机变量阵列加权乘积和完全收敛的条件,并推广了以前学者的结论．     

混合幂的素变数丢番图逼近

证明了,如果λ1,λ2,λ3,λ4是正实数,λ1/λ2是无理数和代数效,V是well-spaced序列,δ>0,那么对于ν,∈V,ν≤X,ε>0,使得|λ1p21+λ2p22+λ3p33+λ4p34-ν|<ν-δ没有素数解P1,p2,p3,p4的ν的个数不超过O(X20/21+2δ+ε).     

关于N-弱鞅和弱鞅不等式的一个注记

$N$-弱鞅($N$-demimartingales)是由Christofides引进的.Christofides指出均值为零的负相协(negatively associated)随机变量序列的部分和序列是$N$-弱鞅. 然而, 相关文献中引理2.1的证明有错误, 这就影响了其文后的结果引理2.2、定理2.1等. 在这篇注记中,修正了相关文献中的引理2.1, 作为应用, 推广了鞅和下鞅的一些结果.     

二元函数极限与一元函数可导关系研究

给出了一元函数y=f(x)在x0可导与二元函数f(x)-f(y)/x-y在(x0,x0)处极限存在等价的条件,并通过反例系统地研究了它们之间的关系,指出了文[1]的错误.