Copula tachnique is a kind of comparatively new method of financial risk analysis, whose core is to connect the co distribution of many random variances with their fringe distributions. This coincides exactly with the...Copula tachnique is a kind of comparatively new method of financial risk analysis, whose core is to connect the co distribution of many random variances with their fringe distributions. This coincides exactly with the method to decompose risks into different components in financial risk analysis.展开更多
Pearson correlation coefficient is very helpful for us to measure the relationship of two random variables.However,there is much localization about Pearson correlation index.In this paper,we propose several new measur...Pearson correlation coefficient is very helpful for us to measure the relationship of two random variables.However,there is much localization about Pearson correlation index.In this paper,we propose several new measurements to evaluate correlation;Moreover,we also point out that there are affiliations with copula.展开更多
In this note, as an example, we introduoe a definition of general optimality in estimating a linear estimable function S<sub>k×p</sub> （S’ μ（X’）） of the mean matrix in multivariate linear mod...In this note, as an example, we introduoe a definition of general optimality in estimating a linear estimable function S<sub>k×p</sub> （S’ μ（X’）） of the mean matrix in multivariate linear model: Y<sub>n×m</sub>=X<sub>n×p</sub> +ε E（ε）=0, Cov（ ）=σ<sup>2</sup>U<sub>n×n</sub> V<sub>m×m</sub>, n≥m. In general, the general optimality of a parametric matrix follows analogously. The above X, S, U≥0 and V≥0 （but V≠0） are known matrix, and σ<sup>2</sup>】0 are unknown parameters, =（ε<sub>1</sub>’, ε<sub>2</sub>’, …, ε<sub>n</sub>’）’, where ε<sub>i</sub> is the ith row of ε, U V denotes the展开更多
文摘Copula tachnique is a kind of comparatively new method of financial risk analysis, whose core is to connect the co distribution of many random variances with their fringe distributions. This coincides exactly with the method to decompose risks into different components in financial risk analysis.
文摘Pearson correlation coefficient is very helpful for us to measure the relationship of two random variables.However,there is much localization about Pearson correlation index.In this paper,we propose several new measurements to evaluate correlation;Moreover,we also point out that there are affiliations with copula.
文摘In this note, as an example, we introduoe a definition of general optimality in estimating a linear estimable function S<sub>k×p</sub> （S’ μ（X’）） of the mean matrix in multivariate linear model: Y<sub>n×m</sub>=X<sub>n×p</sub> +ε E（ε）=0, Cov（ ）=σ<sup>2</sup>U<sub>n×n</sub> V<sub>m×m</sub>, n≥m. In general, the general optimality of a parametric matrix follows analogously. The above X, S, U≥0 and V≥0 （but V≠0） are known matrix, and σ<sup>2</sup>】0 are unknown parameters, =（ε<sub>1</sub>’, ε<sub>2</sub>’, …, ε<sub>n</sub>’）’, where ε<sub>i</sub> is the ith row of ε, U V denotes the
