Humor and satire in politics: Introducing cultural sociology to the field

The landscape of political humor and satire is changing rapidly, and it is becoming an increasingly relevant aspect of our culture. Although scholars have been actively trying to capture this change, majority of the existing frameworks for understanding humor and satire in politics still reduce these phenomena to mere genres or rhetoric tools. In addition, they provide insufficient accounts concerning the reception of humor and satire, and neglect to interpret and explain what they communicate. In the article, a general understanding of humor and satire outside of a political context, followed by an overview of studies discussing humor and satire in politics through their applications in social movements, as leadership tools, and through their manifestations in mass media has been presented. Lastly, a cultural sociological perspective to the field has been introduced. It has been argued that approaching humor and satire in politics through a structural hermeneutic method of the Strong Program will enable us to recognize and treat political humor and satire as autonomous and complex cultural systems which carry an internal power to move people.     

Uniform consistency rate of kNN regression estimation for functional time series data

In this paper, we investigate the k-nearest neighbours (kNN) estimation of nonparametric regression model for strong mixing functional time series data. More precisely, we establish the uniform almost complete convergence rate of the kNN estimator under some mild conditions. Furthermore, a simulation study and an empirical application to the real data analysis of sea surface temperature (SST) are carried out to illustrate the finite sample performances and the usefulness of the kNN approach.     

On the Convergence of the Monte Carlo Maximum Likelihood Method for Latent Variable Models

While much used in practice, latent variable models raise challenging estimation problems due to the intractability of their likelihood. Monte Carlo maximum likelihood (MCML), as proposed by Geyer & Thompson (1992 ), is a simulation-based approach to maximum likelihood approximation applicable to general latent variable models. MCML can be described as an importance sampling method in which the likelihood ratio is approximated by Monte Carlo averages of importance ratios simulated from the complete data model corresponding to an arbitrary value of the unknown parameter. This paper studies the asymptotic (in the number of observations) performance of the MCML method in the case of latent variable models with independent observations. This is in contrast with previous works on the same topic which only considered conditional convergence to the maximum likelihood estimator, for a fixed set of observations. A first important result is that when is fixed, the MCML method can only be consistent if the number of simulations grows exponentially fast with the number of observations. If on the other hand, is obtained from a consistent sequence of estimates of the unknown parameter, then the requirements on the number of simulations are shown to be much weaker.     

与时俱进、创新理论是党的建设的基础——学习江泽民同志《论党的建设》

理论建设是党的建设的首要问题。与时俱进、创新理论是马克思主义基本原理的必然要求,是保持党的先进性的客观需要,是党的建设的永恒主题。     

Estimation of generalized linear latent variable models

Summary.  Generalized linear latent variable models (GLLVMs), as defined by Bartholomew and Knott, enable modelling of relationships between manifest and latent variables. They extend structural equation modelling techniques, which are powerful tools in the social sciences. However, because of the complexity of the log-likelihood function of a GLLVM, an approximation such as numerical integration must be used for inference. This can limit drastically the number of variables in the model and can lead to biased estimators. We propose a new estimator for the parameters of a GLLVM, based on a Laplace approximation to the likelihood function and which can be computed even for models with a large number of variables. The new estimator can be viewed as an M -estimator, leading to readily available asymptotic properties and correct inference. A simulation study shows its excellent finite sample properties, in particular when compared with a well-established approach such as LISREL. A real data example on the measurement of wealth for the computation of multidimensional inequality is analysed to highlight the importance of the methodology.     

一种基于加权F-范数的半正定矩阵的逼近方法

正定性是许多金融预测模型的重要假设前提，然而从实际样本中得到的相关系数矩阵并不能保证其正定性。为此在介绍如何根据样本设定相关系数矩阵以及范数逼近原理的基础上，如何根据该原理找到与之最接近的相关系数矩阵，即最接近的单位对角半正定对称矩阵。通过实证，验证了其方法的有效性。     

局部Haar条件下的变形非线性L逼近

本文讨论了局部Ｈａａｒ条件下变形的非线性Ｌ逼近，得到了包括交错定理在内的特征定理、唯一性和强唯一性定理。     

一类混合指数型算子的一致逼近

主要讨论了一类混合指数型算子的一致逼近问题，并给出了逼近阶的估计和特征刻划。     

具扩充Jacobi结点的Lagrange插值逼近

研究了以扩充Jacobi多项式(1+x)Vn(x)的零点为基点的Lagrange插值多项式Ln(f,x)逼近/k)的一些问题.     

一类多元Gauss-Weierstrass算子线性组合的加权逼近

该文给出了一类多元Gauss-Weierstrass算子线性组合加Jacobi权在一致逼近下的正、逆定理和逼近阶的特征刻划