Partial Order and Extremes of Multivariate Extreme Value Distributions

This paper studies the dependence order among multivariate extreme value dis- tributions with a fixed marginal distribution. Making use of copulas to prove that the set organized by multivariate extreme value distributions and the dependence order defined in it is a partial order set. Finally, the maximum and minimum values of the set is discussed.

Maximal inequalities for demimartingales and their applications

In this paper, we establish some maximal inequalities for demimartingales which generalize and improve the results of Christofides. The maximal inequalities for demimartingales are used as key inequalities to establish other results including Doob’s type maximal inequality for demimartingales, strong laws of large numbers and growth rate for demimartingales and associated random variables. At last, we give an equivalent condition of uniform integrability for demisubmartingales.

An overview of representation theorems for static risk measures

In this paper,we give an overview of representation theorems for various static risk measures:coherent or convex risk measures, risk measures with comonotonic subadditivity or convexity, law-invariant coherent or convex risk measures, risk measures with comonotonic subadditivity or convexity and respecting stochastic orders.

The fundamental theory of abstract majorization inequalities

Using the axiomatic method, abstract concepts such as abstract mean, abstract convex function and abstract majorization are proposed. They are the generalizations of concepts of mean, convex function and majorization, respectively. Through the logical deduction, the fundamental theorems about abstract majorization inequalities are established as follows: for arbitrary abstract mean Σ and $ \Sigma ' $ and abstract ∑ ? $ \Sigma ' $ strict convex function f(x) on the interval I, if x i , y i ∈ I (i = 1, 2,..., n) satisfy that $ (x_1 ,x_2 , \ldots ,x_n ) \prec _n^\Sigma (y_1 ,y_2 , \ldots ,y_n ) $ then $ \Sigma ' $ {f(x 1), f(x 2),..., f(x n )} ? $ \Sigma ' $ {f(y 1), f(y 2),..., f(y n )}. This class of inequalities extends and generalizes the fundamental theorem of majorization inequalities. Moreover, concepts such as abstract vector mean are proposed, the fundamental theorems about abstract majorization inequalities are generalized to n-dimensional vector space. The fundamental theorem of majorization inequalities about the abstract vector mean are established as follows: for arbitrary symmetrical convex set $ \mathcal{S} \subset \mathbb{R}^n $ , and n-variable abstract symmetrical $ \overline \Sigma $ ? $ \Sigma ' $ strict convex function $ \phi (\bar x) $ on $ \mathcal{S} $ , if $ \bar x,\bar y \in \mathcal{S} $ , satisfy $ \bar x \prec _n^\Sigma \bar y $ , then $ \phi (\bar x) \geqslant \phi (\bar y) $ ; if vector group $ \bar x_i ,\bar y_i \in \mathcal{S}(i = 1,2, \ldots ,m) $ satisfy $ \{ \bar x_1 ,\bar x_2 , \ldots ,\bar x_m \} \prec _n^{\bar \Sigma } \{ \bar y_1 ,\bar y_2 , \ldots ,\bar y_m \} $ , then $ \Sigma '\{ \phi (\bar x_1 ),\phi (\bar x_2 ), \ldots ,\phi (\bar x_m )\} \geqslant \Sigma '\{ \phi (\bar y_1 ),\phi (\bar y_2 ), \ldots ,\phi (\bar y_m )\} $ .

Exponential inequalities for associated random variables and strong laws of large numbers

Some exponential inequalities for partial sums of associated random variables are established. These inequalities improve the corresponding results obtained by Ioannides and Roussas (1999), and Oliveira (2005). As application, some strong laws of large numbers are given. For the case of geometrically decreasing covariances, we obtain the rate of convergence n-1/2(log log n)1/2(logn) which is close to the optimal achievable convergence rate for independent random variables under an iterated logarithm, while Ioannides and Roussas (1999), and Oliveira (2005) only got n-1/3(logn)2/3 and n-1/3(logn)5/3, separately.