高维欧氏空间可微函数与复合映射的微分法——链法则

我们将用L(R^n,R^m)表示所有R^n 到R^m内的线性变换组成的集合,其中加法和数乘规定如下:i)加法:若A,B∈L(R^n,R^m),则定义A+B 为(A+B)(X)=A(X)+B(X) (X∈R^n)ii)数乘:若A∈L(R^n,R^m),α∈R^1,则定义αA 为(αA)(X)=αA(X)

A New Latent Class Stochastic Frontier Model for the Best Practices ofUSA＇s Corporate Governance

In this paper, the authors study the association between firms＇ specific characteristics and performances for a sample of 320 American firms using a governance efficiency index, calculated by the stochastic frontier analysis. The use of a latent class in the specification of the model, allowed detecting two groups of firms according to their specific characteristics： the firm size, the leverage, the dividend yield, and the return on equity （ROE）. The results of affectation equation show that the probability to be in the second group （the most efficient） is more important when the firm size, the dividend yield, and the ROE are high, while a high leverage level decreases the chance to be in the first group （the less efficient）.