Maximum likelihood estimation of the linearly structured correlation matrix by a Jacobi-type iterative scheme

Mukherjee and Maiti [Q-procedure for solving likelihood equations in the analysis of covariance structures, Comput. Statist. Quart. 2 (1988), pp. 105–128] proposed an iterative scheme to derive the maximum likelihood estimates of the parameters involved in the population covariance matrix when it is linearly structured. The present investigation provides a Jacobi-type of iterative scheme, MSIII, when the underlying correlation matrix is linearly structured. Such scheme is shown to be quite competent and efficient compared to the prevalent Fisher-scoring (FS) and the Newton–Raphson iterative scheme (NR). An illustrative example is provided for a numerical comparison of the iterates of MSIII, FS and NR choosing the Toeplitz matrix as the population correlation matrix. Numerical behaviour of such schemes is studied in the context of ‘bad’ initial try-out vectors. Additionally a simulation experiment is performed to judge the superiority of MSIII over FS.     

Bayesian analysis of semiparametric Bernstein polynomial regression models for data with sample selection

In regression analysis, a sample selection scheme often applies to the response variable, which results in missing not at random observations on the variable. In this case, a regression analysis using only the selected cases would lead to biased results. This paper proposes a Bayesian methodology to correct this bias based on a semiparametric Bernstein polynomial regression model that incorporates the sample selection scheme into a stochastic monotone trend constraint, variable selection, and robustness against departures from the normality assumption. We present the basic theoretical properties of the proposed model that include its stochastic representation, sample selection bias quantification, and hierarchical model specification to deal with the stochastic monotone trend constraint in the nonparametric component, simple bias corrected estimation, and variable selection for the linear components. We then develop computationally feasible Markov chain Monte Carlo methods for semiparametric Bernstein polynomial functions with stochastically constrained parameter estimation and variable selection procedures. We demonstrate the finite-sample performance of the proposed model compared to existing methods using simulation studies and illustrate its use based on two real data applications.     

列宁对伯恩施坦理论的批判及现实意义

伯恩斯坦理论是第二国际后期兴起的"修正"马克思主义的理论,对该理论的批判不足影响了整个欧洲马克思主义的走向。列宁对伯恩斯坦理论批判推进了马克思主义发展。在当下推进文化大发展中,我们要学习列宁重视非马克思主义理论批判的做法,对一些流行且关乎国家命运的非马克思主义思潮加以批判性研究,在批判中将非马克思主义的理论渊源与实质解读结合起来,使批判与现实结合,弄清问题、服务现实。     

Applied Nonparametric Instrumental Variables Estimation

Instrumental variables are widely used in applied econometrics to achieve identification and carry out estimation and inference in models that contain endogenous explanatory variables. In most applications, the function of interest (e.g., an Engel curve or demand function) is assumed to be known up to finitely many parameters (e.g., a linear model), and instrumental variables are used to identify and estimate these parameters. However, linear and other finite‐dimensional parametric models make strong assumptions about the population being modeled that are rarely if ever justified by economic theory or other a priori reasoning and can lead to seriously erroneous conclusions if they are incorrect. This paper explores what can be learned when the function of interest is identified through an instrumental variable but is not assumed to be known up to finitely many parameters. The paper explains the differences between parametric and nonparametric estimators that are important for applied research, describes an easily implemented nonparametric instrumental variables estimator, and presents empirical examples in which nonparametric methods lead to substantive conclusions that are quite different from those obtained using standard, parametric estimators.     

一类集值映射变分不等式及在凸规划中应用

给定弱连续映射 f:X■X~-*,h:X■R 是下半连续正齐次凸泛函.本文在自反空间中给定 f 某种条件下,得出形如 f(x)+(?)h(x)映射变分不等式与补问题解的存在定理,并在数学规划中得到应用.     

简并能级的三阶微扰修正

讨论了简并能级的三阶微扰修正问题,给出了一阶修正和二阶修正简并部分解除情况下的三阶能量修正,也讨论了一阶修正简并未完全解除时的三阶能量修正.     

阶梯算子和旋量球函数

本文利用转动群的性质，成功地构造了旋量球函数各个量子数的阶梯算子。由此得到了归一化的旋量球函数的微分表达式和一系列的递推公式。     

仿射李代数C_3~((1))的一类顶点表示

通过引进一系列顶点算子,构造了访射李代数C_3~((I))的一类水平为1的不可约最高权表示。     

二维广义Bernstein多项式

给出二维广义的Bernstein多项式的定义 ,并研究该多项式一致收敛的充分必要条件 ,用连续模估计该多项式对连续函数的逼近误差 .     

初等表示的权的标积

半单李代数的素根可以用适当的初等表示的权来表示,反过来,初等表示的权也可以用素根来表示。在适当地选取了半单李代数的根系之后,就可算出初等表示权的标积。本文利用Cartan矩阵给出了一种计算半单李代数初等表示的权的标积的方法,并由此算出了二次Casimir算符的本征值。