Mean Time to Failure of Weighted k-out-of-n: G Systems

The purpose of this article is to develop a Monte-Carlo simulation algorithm for computing mean time to failure (MTTF) of weighted-k-out-of-n:G and linear consecutive-weighted-k-out-of-n:G systems. Our algorithm is based on the use of appropriately defined stochastic process which represents the total weight of the system at time t. These stochastic processes are explicitly defined and used along with the ordered component lifetimes to simulate MTTF of the systems with weighted components.     

近十五年西方历史学的新发展

20世纪后期,历史研究的重点继续是从分析的社会科学转移到更加强调文化因素的方面来,但是面临着迅速变化的世界局面,却具有其更加繁富且已改变了的焦点。历史学家并未受到后现代主义太多的影响,但关于应该如何重铸过去的问题的确更趋复杂了。在历史学思想与实践的重新定向过程中,人们更为深入地反思和质疑了历史作为引向今日西方文明的一种单向历程的观念,政治领域和阶级的概念扩大或改变了,妇女与性别的历史日益被纳入到通史系统中来。而在迫切需要另行考察现代性以及撰写全球史的状况面前,我们对于广泛跨文化的方法仍然缺乏综合的认知,有待于未来的进一步规划。     

黑色幽默小说视阈中的战争书写--以《第二十二条军规》为中心

20世纪六七十年代的美国黑色幽默小说对战争题材情有独钟。基于作家特有的人生经验、现实感受、思想观念和美学倾向,他们对于战争的描绘与开掘别开生面。和现实主义小说对战争的表现不同,在黑色幽默小说的世界里,战争被赋予了更为荒诞的色彩和更接近存在本质的寓意。《第二十二条军规》作为其中的典范之作,从小说内容与形式的处理手法和主题立意的表达上,均凸现出这一流派独特的风格、观念。战争在该小说中通过作战部队里形形色色癫狂古怪的人物以及各种匪夷所思的离奇事件,呈现出遍布混乱与疯狂的奇异景象 而透过对掩盖在谎言与制度之下的恐怖与荒谬的揭示,战争的无理性与无意义昭然若揭。     

A model of the Canadian global exchange rate: A test of the 1970s

This comment points out some methodological weaknesses in the Canadian global exchange rate model and the associated efficiency tests of Marwah and Bodkin. The model is found to be inadequate for policy analysis.     

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.     

Conditional variance estimation via nonparametric generalized additive models

Generalized additive models provide a way of circumventing curse of dimension in a wide range of nonparametric regression problem. In this paper, we present a multiplicative model for conditional variance functions where one can apply a generalized additive regression method. This approach extends Fan and Yao (1998) to multivariate cases with a multiplicative structure. In this approach, we use squared residuals instead of using log-transformed squared residuals. This idea gives a smaller variance than Yu (2017) when the variance of squared error is smaller than the variance of log-transformed squared error. We provide estimators based on quasi-likelihood and an iterative algorithm based on smooth backfitting for generalized additive models. We also provide some asymptotic properties of estimators and the convergence of proposed algorithm. A numerical study shows the empirical evidence of the theory.