The marked empirical process to test nonlinear time series against a large class of alternatives when the random vectors are nonstationary and absolutely regular

In this paper, we propose a method for testing absolutely regular and possibly nonstationary nonlinear time-series, with application to general AR-ARCH models. Our test statistic is based on a marked empirical process of residuals which is shown to converge to a Gaussian process with respect to the Skohorod topology. This testing procedure was first introduced by Stute [Nonparametric model checks for regression, Ann. Statist. 25 (1997), pp. 613–641] and then widely developed by Ngatchou-Wandji [Weak convergence of some marked empirical processes: Application to testing heteroscedasticity, J. Nonparametr. Stat. 14 (2002), pp. 325–339; Checking nonlinear heteroscedastic time series models, J. Statist. Plann. Inference 133 (2005), pp. 33–68; Local power of a Cramer-von Mises type test for parametric autoregressive models of order one, Compt. Math. Appl. 56(4) (2008), pp. 918–929] under more general conditions. Applications to general AR-ARCH models are given.     

The Absolute Difference Law For Expectations

We revisit the addition law for expectations and present a sibling law: the absolute law for expectations. We show that these two laws and their corresponding laws for probabilities can be reconciled under a single framework. As an application, we use the absolute law for expectations to calculate the mean absolute deviation. Finally, we remark on a hidden point in a related article previously published on these pages; this will help readers to avoid a potential pitfall.     

方言幽默话语解读中的预设分析

预设理论是语言学理论的重要组成部分,被广泛应用于言语交际活动中。作为特殊的言语交际形式,方言幽默又与普通幽默不同,它更多地与本土文化相联系,具有地域性特点。因此,预设理论的应用相应地更加复杂。文章将以方言幽默为素材,深入分析预设理论的应用,并在理解预设理论的基础上探讨方言幽默话语生成原因的独特之处。     

进入壁垒的七种基本定义及其意蕴比较

进入壁垒是研究市场定义和市场势力的核心概念。自从贝恩提出这一概念以来,进入壁垒主要有七种基本定义,其定义有不同的缺点及其效率含义。经济学家围绕进入壁垒的含义进行了大量深入分析,但是对进入壁垒的基本含义、判定标准和主要形式等方面还存在实质性的分歧。     

康德到谢林:主客体矛盾的呈现和在主观中的消解

康德以认识论上的哥白尼革命开启了德国的主体性哲学,但康德、费希特将主体和客体的关系片面化为客体对主体的服从,就远离了启蒙运动以来的唯物主义原则,谢林试图将这一唯物主义原则补充进哲学。谢林既肯定康德的主体中的纯粹理性的存在,又认为客体中存在同样的客观理性,认为这两者具有"先定的和谐",两者是绝对同一的。谢林认为这种先定的和谐只有少数天才在"理智"、"直观"和"美感"中才能把握。谢林将主客体的矛盾内化为主体内在的感觉,并在感觉中、在艺术中消解这种矛盾,使哲学和艺术成了一种纯粹个人的东西,哲学和艺术失去客观性和普遍性,走上了通往神秘主义的路径。     

英语中独立主格结构的再分析

从独立主格的定义和演变过程出发,从一个完整的角度探讨英语中独立主格结构这一独特语法现象的特点、功能及应用,并对相关的一些易混淆的语法现象作出分析和比较。     

一种新的灰色绝对关联度模型及其应用

为了处理序列曲线间的负相关等问题,并使关联满足一定的性质,基于灰色关联分析的基本思想,本文利用数列曲线的平均相对变化态势构建了一种新的灰色绝对关联度模型,并探讨了模型的唯一性、对称性、相似性、平行性、一致性等性质,而后以新模型对我国及各省市区产业结构的有序度进行了测算,结果表明新模型计算简单,计算量小,更与实际相符。     

Statistical and computational tradeoff in genetic algorithm-based estimation

When a genetic algorithm (GA) is employed in a statistical problem, the result is affected by both variability due to sampling and the stochastic elements of algorithm. Both of these components should be controlled in order to obtain reliable results. In the present work we analyze parametric estimation problems tackled by GAs, and pursue two objectives: the first one is related to a formal variability analysis of final estimates, showing that it can be easily decomposed in the two sources of variability. In the second one we introduce a framework of GA estimation with fixed computational resources, which is a form of statistical and the computational tradeoff question, crucial in recent problems. In this situation the result should be optimal from both the statistical and computational point of view, considering the two sources of variability and the constraints on resources. Simulation studies will be presented for illustrating the proposed method and the statistical and computational tradeoff question.     

Selecting the regularization parameters in high-dimensional panel data models: Consistency and efficiency

This article considers panel data models in the presence of a large number of potential predictors and unobservable common factors. The model is estimated by the regularization method together with the principal components procedure. We propose a panel information criterion for selecting the regularization parameter and the number of common factors under a diverging number of predictors. Under the correct model specification, we show that the proposed criterion consistently identifies the true model. If the model is instead misspecified, the proposed criterion achieves asymptotically efficient model selection. Simulation results confirm these theoretical arguments.     

Regularization and variable selection for infinite variance autoregressive models

Autoregressive models with infinite variance are of great importance in modeling heavy-tailed time series and have been well studied. In this paper, we propose a penalized method to conduct model selection for autoregressive models with innovations having Pareto-like distributions with index α∈(0,2)$\alpha \in (\mathrm{0,2})$. By combining the least absolute deviation loss function and the adaptive lasso penalty, the proposed method is able to consistently identify the true model and at the same time produce efficient estimators with a convergence rate of n^−1/α$n^{-1/\alpha }$. In addition, our approach provides a unified way to conduct variable selection for autoregressive models with finite or infinite variance. A simulation study and a real data analysis are conducted to illustrate the effectiveness of our method.