Frequency domain bootstrap for ratio statistics under long-range dependence

A frequency domain bootstrap (FDB) is a common technique to apply Efron’s independent and identically distributed resampling technique (Efron, 1979) to periodogram ordinates – especially normalized periodogram ordinates – by using spectral density estimates. The FDB method is applicable to several classes of statistics, such as estimators of the normalized spectral mean, the autocorrelation (but not autocovariance), the normalized spectral density function, and Whittle parameters. While this FDB method has been extensively studied with respect to short-range dependent time processes, there is a dearth of research on its use with long-range dependent time processes. Therefore, we propose an FDB methodology for ratio statistics under long-range dependence, using semi- and nonparametric spectral density estimates as a normalizing factor. It is shown that the FDB approximation allows for valid distribution estimation for a broad class of stationary, long-range (or short-range) dependent linear processes, without any stringent assumptions on the distribution of the underlying process. The results of a large simulation study show that the FDB approximation using a semi- or nonparametric spectral density estimator is often robust for various values of a long-memory parameter reflecting magnitude of dependence. We apply the proposed procedure to two data examples.     

Revisiting the cooperation–competition paradox: A configurational approach to short- and long-term coopetition performance in business networks

Cooperation and competition are often viewed as incompatible, antagonistic forces, thus are operationalized as two extremes on a continuum. However, they can coexist and even enable each other, thus may be operationalized as orthogonal constructs. We address this contradictory phenomenon by developing a more granular view of the cooperation–competition paradox. Building on interdisciplinary research, we develop a three-dimensional model of relational space (fairness–opportunism, sharing–control, and engagement–rivalry), providing a novel tool with which to investigate the paradoxical interplay between cooperation and competition through eight operationalizable configurations. Using fuzzy-set qualitative comparative analysis (fsQCA), we test our model by assessing how different configurations of interfirm relationships influence the short- and long-term success of a sample of 217 firms. Our findings show that only two of the eight possible relational configurations are associated with firm success, one in both the long and short term, and the other in the short term only.     

强束流Zr离子注入H13钢改善其耐磨性研究

采用由金属蒸汽真空弧离子源引出的强束流Ｚｒ,Ｚｒ＋Ｃ离子对Ｈ１３钢进行了离子束表面处理,借助划痕仪和摩擦试验机分别测量了经表面处理的Ｈ１３钢的耐磨性和摩擦系数。研究结果表明：经Ｚｒ,Ｚｒ＋Ｃ离子束处理的Ｈ１３钢的耐磨性有所提高,摩擦系数明显降低,采用卢瑟福背散射谱（ＲＢＳ）ｘ射线衍射分析了注入层的成分,结构及影响其耐磨性的因素。     

3D打印著作权法律问题探究

面对3D打印技术发展进程中日益凸显的著作权法律问题,阐释3D打印数字模型在著作权法中的法律界定及保护范围,对3D打印数字模型究竟属于何种作品进行了探讨;指出3D数字模型到3D打印物的转换应为复制,以及3D数字模型和3D打印物的著作权侵权风险问题;提出应兼顾各方利益,对3D打印的著作权保护要在私人利益与公共利益之间寻找新的平衡.     

全球治理视角下G20贸易救济现状与特性分析

以倾向性贸易救济为代表的贸易保护主义对世界经济造成了强烈冲击,成为全球重点治理的领域。鉴于现时期G20的显著影响力和重要地位,从整体概况、调查与被诉国别、产品类别等层面,对G20贸易救济现状与特性进行梳理和分析,并提出G20参与全球贸易协调治理的对策建议,对于完善全球治理机制,推动国际贸易可持续发展具有重要实践意义。     

Assessing additivity in nonparametric models —A kernel‐based method

In this article, we address the testing problem for additivity in nonparametric regression models. We develop a kernel‐based consistent test of a hypothesis of additivity in nonparametric regression, and establish its asymptotic distribution under a sequence of local alternatives. Compared to other existing kernel‐based tests, the proposed test is shown to effectively ameliorate the influence from estimation bias of the additive component of the nonparametric regression, and hence increase its efficiency. Most importantly, it avoids the tuning difficulties by using estimation‐based optimal criteria, while there is no direct tuning strategy for other existing kernel‐based testing methods. We discuss the usage of the new test and give numerical examples to demonstrate the practical performance of the test. The Canadian Journal of Statistics 39: 632–655; 2011. © 2011 Statistical Society of Canada     

Two‐sample empirical likelihood ratio tests for medians in application to biomarker evaluations

The median is a commonly used parameter to characterize biomarker data. In particular, with two vastly different underlying distributions, comparing medians provides different information than comparing means; however, very few tests for medians are available. We propose a series of two‐sample median‐specific tests using empirical likelihood methodology and investigate their properties. We present the technical details of incorporating the relevant constraints into the empirical likelihood function for in‐depth median testing. An extensive Monte Carlo study shows that the proposed tests have excellent operating characteristics even under unfavourable occasions such as non‐exchangeability under the null hypothesis. We apply the proposed methods to analyze biomarker data from Western blot analysis to compare normal cells with bronchial epithelial cells from a case–control study. The Canadian Journal of Statistics 39: 671–689; 2011. © 2011 Statistical Society of Canada     

Testing the monotonicity or convexity of a function using regression splines

Methods for smoothed isotonic or convex regression are useful in many applications. Sometimes the shape assumptions constitute a priori knowledge about the regression function, but often the shape is part of the research question. The authors propose tests for monotonicity and convexity using constrained and unconstrained regression splines. The tests have good large‐sample properties and the small‐sample behaviour is illustrated through simulations. Extensions to the partial linear model and the generalized regression model are presented. The Canadian Journal of Statistics 39: 89–107; 2011 © 2011 Statistical Society of Canada     

A note on testing the regression functions via nonparametric smoothing

The authors present a consistent lack‐of‐fit test in nonlinear regression models. The proposed procedure possesses some nice properties of Zheng's test such as the consistency, the ability to detect any local alternatives approaching the null at rates slower than the parametric rate. What's more, for a predetermined kernel function, the proposed test is more powerful than Zheng's test and the validity of these findings is confirmed by the simulation studies and a real data example. In addition, the authors find out a close connection between the choices of normal kernel functions and the bandwidths. The Canadian Journal of Statistics 39: 108–125; 2011 © 2011 Statistical Society of Canada     

A note on Läuter-type robust design for polynomial regression models

In this paper, we consider the problem of model robust design for simultaneous parameter estimation among a class of polynomial regression models with degree up to k. A generalized D-optimality criterion, the Ψ_α‐optimality criterion, first introduced by Läuter (1974) is considered for this problem. By applying the theory of canonical moments and the technique of maximin principle, we derive a model robust optimal design in the sense of having highest minimum Ψ_α‐efficiency. Numerical comparison indicates that the proposed design has remarkable performance for parameter estimation in all of the considered rival models.