Theoretical results on the discrete Weibull distribution of Nakagawa and Osaki

In this paper, we discuss some theoretical results and properties of the discrete Weibull distribution, which was introduced by Nakagawa and Osaki [The discrete Weibull distribution. IEEE Trans Reliab. 1975;24:300–301]. We study the monotonicity of the probability mass, survival and hazard functions. Moreover, reliability, moments, p-quantiles, entropies and order statistics are also studied. We consider likelihood-based methods to estimate the model parameters based on complete and censored samples, and to derive confidence intervals. We also consider two additional methods to estimate the model parameters. The uniqueness of the maximum likelihood estimate of one of the parameters that index the discrete Weibull model is discussed. Numerical evaluation of the considered model is performed by Monte Carlo simulations. For illustrative purposes, two real data sets are analyzed.     

Approximate composite marginal likelihood inference in spatial generalized linear mixed models

Non-Gaussian spatial responses are usually modeled using spatial generalized linear mixed model with spatial random effects. The likelihood function of this model cannot usually be given in a closed form, thus the maximum likelihood approach is very challenging. There are numerical ways to maximize the likelihood function, such as Monte Carlo Expectation Maximization and Quadrature Pairwise Expectation Maximization algorithms. They can be applied but may in such cases be computationally very slow or even prohibitive. Gauss–Hermite quadrature approximation only suitable for low-dimensional latent variables and its accuracy depends on the number of quadrature points. Here, we propose a new approximate pairwise maximum likelihood method to the inference of the spatial generalized linear mixed model. This approximate method is fast and deterministic, using no sampling-based strategies. The performance of the proposed method is illustrated through two simulation examples and practical aspects are investigated through a case study on a rainfall data set.     

Bayesian inference for stable Lévy–driven stochastic differential equations with high‐frequency data

In this paper, we consider parametric Bayesian inference for stochastic differential equations driven by a pure‐jump stable Lévy process, which is observed at high frequency. In most cases of practical interest, the likelihood function is not available; hence, we use a quasi‐likelihood and place an associated prior on the unknown parameters. It is shown under regularity conditions that there is a Bernstein–von Mises theorem associated to the posterior. We then develop a Markov chain Monte Carlo algorithm for Bayesian inference, and assisted with theoretical results, we show how to scale Metropolis–Hastings proposals when the frequency of the data grows, in order to prevent the acceptance ratio from going to zero in the large data limit. Our algorithm is presented on numerical examples that help verify our theoretical findings.     

Prediction of future failures times based on Type-I hybrid censored samples of random sample sizes

In this article, we consider the prediction of future failure times based on Type-I hybrid censored samples. Point predictors and prediction intervals using different procedures are discussed for a general model. The exponential and Rayleigh distributions are used as illustrative examples to show the most simplified forms of the so obtained predictors as well as prediction intervals. Intensive simulation study and a real life dataset are presented to illustrate our findings and results.     

Efficient Bayesian inference in generalized inverse gamma processes for stochastic volatility

This paper develops a novel and efficient algorithm for Bayesian inference in inverse Gamma stochastic volatility models. It is shown that by conditioning on auxiliary variables, it is possible to sample all the volatilities jointly directly from their posterior conditional density, using simple and easy to draw from distributions. Furthermore, this paper develops a generalized inverse gamma process with more flexible tails in the distribution of volatilities, which still allows for simple and efficient calculations. Using several macroeconomic and financial datasets, it is shown that the inverse gamma and generalized inverse gamma processes can greatly outperform the commonly used log normal volatility processes with Student’s t errors or jumps in the mean equation.     

Reconsidering the philanthropic foundation minimum payout policy under a “new normal”

With the increasing salience of foundations in many policy fields, and recent changes in market conditions, policies towards foundations designed decades ago seem outdated. In this article we suggest reassessing foundation payout minimums. To examine the impact of payout rates on grantmaking foundations lifespan and performance under “new normal” economics, we simulate multiple foundations lifecycles using Monte Carlo methods in diverse capital market conditions, with varied investment and payout strategies.We find that while under past market regime perpetuity seems to be a given, under more probable future scenarios, foundations might face increasingly early mortality and endowment depletion, limiting their potential impact. Furthermore, lower payout rates allow for higher lifetime grantmaking, higher mean annual grantmaking, and lower giving volatility. Accordingly, we suggest a tiered payout policy, in line with foundations’ missions and proper financial planning.     

远海岛礁冷链物流体系优化模型及其集成算法

为研究远海岛礁渔获冷链物流的运行方案,本文选择了岛礁冷库储存中转和海上直接收购等两种不同的渔获运输方式,同时考虑了中转冷库选址、中转运输和直接收购船舶配备、航线配置、在渔场停留时间和渔获运输损耗等诸多复杂问题,构建了远海冷链物流体系组合优化模型。求解时,提出了一种以遗传算法（GA）为外部框架,以改进的模拟植物生长算法（PGSA）为内部模块,通过信息传递实现内外连接交互的集成算法。最后,以我国南海远海岛礁及渔场为实例进行分析,取得了较好效果。通过与传统遗传算法对比,本文优化结果、稳定性及计算速度等均有明显提高,从而证明了本文所建模型与算法的合理性和有效性。     

基于个性化需求的产品竞争供应链结构选择

基于"制销分离"与"定制一体"两种结构选择,构建个性化产品供应链处于非竞争与竞争环境下的博弈模型,在引入一个实际案例的基础上,运用算例仿真比较分析两种运营模式下的供应链运作策略和盈利差异。结果显示：与制销分离结构相比,个性化产品供应链选择定制一体结构,有利于匹配产品个性化制造、提升产品个性化水平和市场需求、增加供应链的期望收益;个性化产品供应链选择制销分离结构时,适度的批发价格激励能够提升产品个性化水平、更好满足消费者个性化需求、改善供应链的运营绩效,分销商适度让利加大批发价格激励力度可显著促进产品个性化制造互动、提升产品个性化水平;竞争将消减个性化产品供应链的运营绩效,但选择定制一体结构可显著提升竞争力。     

网络外部性对双渠道供应链信息分享的影响

研究了网络外部性对双渠道供应链信息分享的影响。分别建立了存在网络外部性和不存在网络外部性下的双渠道供应链模型,通过比较零售商信息分享和不分享下其与制造商的期望利润。研究发现：当未考虑产品的网络外部性时,零售商不与制造商分享其市场需求信息,与已有研究一致。当考虑了产品网络外部性且网络外部性较小时,零售商仍不与制造商分享市场需求信息;然而,当网络外部性较大时,零售商与制造商分享其市场需求信息。此外,零售商与制造商共享其市场需求信息有助于增加制造商和供应链利润。     

多层供应链外包模式的选择及其协调

本文研究由两个原始设备制造企业（品牌企业）、一个代工企业和一个供应商组成的多层供应链的外包模式选择问题。应用主从博弈和纳什博弈理论,当一个原始设备制造企业的外包模式给定后,通过对不同外包模型求解比较,给出了后跟进的原始设备制造企业外包模式的最优选择策略。研究发现：后跟进的原始设备制造企业最优策略应采取与前一个原始设备制造企业相同的外包策略。针对最优外包策略,还设计了使供应链达到协调的二部定价契约机制,提高了外包供应链的竞争力。