New Families of Copulas Based on Periodic Functions

Abstract

Although there exists a large variety of copula functions, only a few are practically manageable, and often the choice in dependence modeling falls on the Gaussian copula. Furthermore most copulas are exchangeable, thus implying symmetric dependence. We introduce a way to construct copulas based on periodic functions. We study the two-dimensional case based on one dependence parameter and then provide a way to extend the construction to the n-dimensional framework. We can thus construct families of copulas in dimension n and parameterized by n ? 1 parameters, implying possibly asymmetric relations. Such “periodic” copulas can be simulated easily.     

中国农村居民的健康动态研究

 利用四轮中国健康与营养状况调查构成的面板数据（1997-2006）,本文研究了中国农村居民的健康动态决定过程及健康持续性。我们使用动态随机效应probit模型控制了个体的不可观测异质性。同时,为了避免状态依赖效应的估计偏差,本文分别采纳了Heckman(1981)和Wooldridge(2005)提出的估计方法来处理“初始条件问题”。结论表明：在控制了其他因素之后,状态依赖效应对中国农村居民健康状况具有显著的影响;此外,那些年龄较大、教育水平较低、收入水平较低的农村弱势群体陷入持续性健康问题的可能性更大。     

组织冲突的结构因素研究

本文通过问卷调查的方式对组织冲突、组织结构因素以及它们之间的关系等方面的假设进行了实证考察,指出了组织冲突水平的衡量标准、管理层次、生命周期阶段、业务流程依存性等组织结构因素与组织冲突的关系。最后,提出了在组织冲突和组织结构因素等方面的管理政策建议。     

Exponential random graph model specifications for bipartite networks—A dependence hierarchy

In this paper, we review the development of dependence structures for exponential random graph models for bipartite networks, and propose a hierarchy of dependence structures within which different dependence assumptions may be located. Based on this hierarchy, we propose a new set of model specifications by including bipartite graph configurations involving more than four nodes. We discuss the theoretical significance of the various effects that the extended models afford, and illustrate application of this hierarchy of models to several bipartite networks related to the political mobilization in Brazil in the early 1990s (Mische, 2007).     

Drivers and Performance Outcomes of Supplier Innovation Generation in Customer–Supplier Relationships: The Role of Power‐Dependence

While innovations generated by supply channel relationships, as opposed to individual partners, play an increasingly important role in the success of all supply chain partners, there has been a dearth of research in the literature on how supply chain relationships cultivate the process of such innovation generation. We explore supplier market knowledge acquisition, relationship learning, systems collaboration, and technological uncertainty as antecedents of supplier innovation generation, which is in turn hypothesized to positively affect the relationship performance of the supplier. Furthermore, supplier dependence on the buyer is investigated as a moderator of the effects of such antecedents on supplier innovation generation. Empirical tests, which used a sample of 236 Taiwanese executives, supported most of the hypotheses, and some implications of the results are discussed.     

On a perturbed dual risk model with dependence between inter-gain times and gain sizes

The dual risk model may be used to model the revenue process of a company with constant expense rate and occasional gains. In this paper, we consider a dual risk model with both inter-gain times and expense rates depending on the size of previous gain. Also, we assume the process is perturbed by a Brownian motion. Exact solutions for the Laplace transform and the first moment of the time to ruin with arbitrary gain-size distribution are obtained. Applications with numerical illustrations are provided to examine the impacts of the dependence structure and perturbation.     

More Powerful Control of the False Discovery Rate Under Dependence

In a breakthrough paper, Benjamini and Hochberg (J Roy Stat Soc Ser B 57:289–300, 1995) proposed a new error measure for multiple testing, the FDR; and developed a distribution-free procedure to control it under independence among the test statistics. In this paper we argue by extensive simulation and theoretical considerations that the assumption of independence is not needed. Along the lines of (Ann Stat 32:1035–1061, 2004b), we moreover provide a more powerful method, that exploits an estimator of the number of false nulls among the tests. We propose a whole family of iterative estimators that prove robust under dependence and independence between the test statistics. These estimators can be used to improve also classical multiple testing procedures, and in general to estimate the weight of a known component in a mixture distribution. Innovations are illustrated by simulations.     

考虑截面相关条件下的异质性面板数据协整回归模型的估计

 面板数据的非平稳分析是近年来迅速发展的方向,其中考虑截面相关情形下面板数据的协整分析的发展备受关注。Bai &; Kao（2006）得出了截面相关条件下面板协整估计的因子模型,但该模型只考虑了被解释变量截面相关情形,未考虑解释变量的截面相关,且假定各截面间长期协方差矩阵相同。本文在Bai（2006）考虑截面相关条件下面板数据协整回归模型估计的基础上将其结论推广至被解释变量和解释变量均截面相关及截面长期协方差矩阵不相同即异质性时的情形,并试图通过Monte Carlo 模拟讨论其小样本性质。并且由于截面间长期协方差矩阵异质性的存在,本文还针对两变量的协整系统提出了系数检验的组间均值t统计量。     

On Dependence Properties of Random Minima and Maxima

Let X _(n) and X ₍₁₎ be the largest and smallest order statistics, respectively, of a random sample of fixed size n. Quite generally, X ₍₁₎ and X _(n) are approximately independent for n sufficiently large. In this article, we study the dependence properties of random extremes in terms of their copula, when the sample size has a left-truncated binomial distribution and show that they tend to be more dependent in this case. We also give closed-form formulas for the measures of association Kendall's τ and Spearman's ρ to measure the amount of dependence between two extremes.     

On Robustness of Model-Based Bootstrap Schemes in Nonparametric Time Series Analysis

Theory in time series analysis is often developed under the assumption of finite-dimensional models for the data generating process. Whereas corresponding estimators such as those of a conditional mean function are reasonable even if the true dependence mechanism is more complex, it is usually necessary to capture the whole dependence structure asymptotically for the bootstrap to be valid. In contrast, we show that certain simplified bootstrap schemes which imitate only some aspects of the time series are consistent for quantities arising in nonparametric statistics. To this end, we generalize the well-known "whitening by windowing" principle to joint distributions of nonparametric estimators of the autoregression function. Consequently, we obtain that model-based nonparametric bootstrap schemes remain valid for supremum-type functionals as long as they mimic those finite-dimensional joint distributions consistently which determine the quantity of interest. As an application, we show that simple regression-type bootstrap schemes can be applied for the determination of critical values for nonparametric tests of parametric or semiparametric hypotheses on the autoregression function in the context of a general process.