On the survival time of a repairable duplex system sustained by a cold standby unit subjected to a priority rule

We analyze the survival time of a general duplex system sustained by a cold standby unit subjected to a priority rule. The analysis is based on advanced complex function theory (sectionally holomorphic functions). As an example, we consider Weibull–Gnedenko and Erlang distributions for failure and repair. Several graphs are displaying the survival function.     

Young Children as Wayfarers: Learning about Place by Moving Through It

This article describes the walking and moving of young children around places. It is based on an ethnographic study of children aged between 24 and 36 months visiting a museum. Drawing on Ingold's (2007) concept of wayfaring, the author argues movement through place creates embodied, tacit ways of knowing and experiencing the world. This embodied and tacit knowledge is not well‐accounted for in dominant models of how young children learn. In this study, wayfaring both enabled the children to learn about places and routes, and led to the development of traditions, in which collective meanings and actions were attached to particular locations.     

Inference for Weibull distribution under adaptive Type-I progressive hybrid censored competing risks data

In this paper, a competing risks model is considered under adaptive type-I progressive hybrid censoring scheme (AT-I PHCS). The lifetimes of the latent failure times have Weibull distributions with the same shape parameter. We investigate the maximum likelihood estimation of the parameters. Bayes estimates of the parameters are obtained based on squared error and LINEX loss functions under the assumption of independent gamma priors. We propose to apply Markov Chain Monte Carlo (MCMC) techniques to carry out a Bayesian estimation procedure and in turn calculate the credible intervals. To evaluate the performance of the estimators, a simulation study is carried out.     

Reversible jump Markov chain Monte Carlo algorithms for Bayesian variable selection in logistic mixed models

In this article, to reduce computational load in performing Bayesian variable selection, we used a variant of reversible jump Markov chain Monte Carlo methods, and the Holmes and Held (HH) algorithm, to sample model index variables in logistic mixed models involving a large number of explanatory variables. Furthermore, we proposed a simple proposal distribution for model index variables, and used a simulation study and real example to compare the performance of the HH algorithm with our proposed and existing proposal distributions. The results show that the HH algorithm with our proposed proposal distribution is a computationally efficient and reliable selection method.     

A discrete-time risk model with Poisson ARCH claim-number process

Abstract

In this paper, we propose a discrete-time risk model with the claim number following an integer-valued autoregressive conditional heteroscedasticity (ARCH) process with Poisson deviates. In this model, the current claim number depends on the previous observations. Within this framework, the equation for finding the adjustment coefficient is derived. Numerical studies are also carried out to examine the impact of the Poisson ARCH dependence structure on the ruin probability.     

Countering Overseas Power in Global Value Chains: Information Asymmetries and Subcontracting in the Plastics Industry

Resource Dependency Theory (RDT) and Global Value Chain (GVC) analysis have been deployed in the strategic and international management literatures to address questions of power in dyadic relationships and global production networks, respectively. This paper integrates the two theoretical approaches in order to expand RDT, using insights from Hirschman's exit/voice model to show the options available to some firms but not others. Using the relationship between buyers and contract manufacturers from GVC analysis, we find a correlation between firm size and choice of strategic action in response to contract manufacturers' dependence on buyers. Large firms follow an acquiescence strategy while small manufacturers follow an avoidance strategy, able to use both exit and voice strategies. Enabled by scale or control over information, both of these approaches successfully reduce uncertainty and provide a source of sustained competitive advantage. Using a study of the production chain in consumer plastics manufacturing in China, we show how dependent firms respond to GVC induced pressure. We find that based on the size of the contract manufacturer, the range of strategic responses to power is constrained by the nature of the dependency in global value chains. This opens important insights into the role that structural characteristics of organizations (like size) play in determining strategic freedom.     

Estimation and Properties of a Time-Varying EGARCH(1,1) in Mean Model

Time-varying GARCH-M models are commonly employed in econometrics and financial economics. Yet the recursive nature of the conditional variance makes likelihood analysis of these models computationally infeasible. This article outlines the issues and suggests to employ a Markov chain Monte Carlo algorithm which allows the calculation of a classical estimator via the simulated EM algorithm or a simulated Bayesian solution in only O(T) computational operations, where T is the sample size. Furthermore, the theoretical dynamic properties of a time-varying-parameter EGARCH(1,1)-M are derived. We discuss them and apply the suggested Bayesian estimation to three major stock markets.     

Estimation for seasonal fractional ARIMA with stable innovations via the empirical characteristic function method

Seasonal fractional ARIMA (ARFISMA) model with infinite variance innovations is used in the analysis of seasonal long-memory time series with large fluctuations (heavy-tailed distributions). Two methods, which are the empirical characteristic function (ECF) procedure developed by Knight and Yu [The empirical characteristic function in time series estimation. Econometric Theory. 2002;18:691–721] and the Two-Step method (TSM) are proposed to estimate the parameters of stable ARFISMA model. The ECF method estimates simultaneously all the parameters, while the TSM considers in the first step the Markov Chains Monte Carlo–Whittle approach introduced by Ndongo et al. [Estimation of long-memory parameters for seasonal fractional ARIMA with stable innovations. Stat Methodol. 2010;7:141–151], combined with the maximum likelihood estimation method developed by Alvarez and Olivares [Méthodes d'estimation pour des lois stables avec des applications en finance. Journal de la Société Française de Statistique. 2005;1(4):23–54] in the second step. Monte Carlo simulations are also used to evaluate the finite sample performance of these estimation techniques.     

延安时期党内政治生态建设理论创新及其时代价值

延安时期,中国共产党的领袖们在党内政治生态建设方面进行了大量卓有成效的理论创新,阐明了党内政治生态建设的内在动力、基本规范、重要途径和根本保障。当前,我们党在全面从严治党过程中,严肃党内政治生活,净化党内政治生态,仍需要从延安时期党内政治生态建设历史经验中汲取营养。