Evidence of bias in the Eurovision song contest: modelling the votes using Bayesian hierarchical models

The Eurovision Song Contest is an annual musical competition held among active members of the European Broadcasting Union since 1956. The event is televised live across Europe. Each participating country presents a song and receive a vote based on a combination of tele-voting and jury. Over the years, this has led to speculations of tactical voting, discriminating against some participants and thus inducing bias in the final results. In this paper we investigate the presence of positive or negative bias (which may roughly indicate favouritisms or discrimination) in the votes based on geographical proximity, migration and cultural characteristics of the participating countries through a Bayesian hierarchical model. Our analysis found no evidence of negative bias, although mild positive bias does seem to emerge systematically, linking voters to performers.     

利用数学软件复原碎纸片的方法

研究了碎纸片的拼接复原问题,建立了合理有效的碎纸片拼接复原的数学模型；给出了数学软件实现自动拼接的具体算法；结合人工拼接得出复原图片；最后,对所给的拼接方法提出了改进方向.     

Diagnostics for quasi-likelihood nonlinear models

Quasi-likelihood nonlinear models (QLNMs) are an extension of generalized linear model and include a widen class of models as special cases. This article investigates some diagnostic methods in QLNMs. An equivalency between a case-deletion model and a mean-shift outlier model in QLNM is established. Two simulation study and a real dataset are used to illustrate the proposed diagnostic methods.     

Marginal zero-inflated regression models for count data

Data sets with excess zeroes are frequently analyzed in many disciplines. A common framework used to analyze such data is the zero-inflated (ZI) regression model. It mixes a degenerate distribution with point mass at zero with a non-degenerate distribution. The estimates from ZI models quantify the effects of covariates on the means of latent random variables, which are often not the quantities of primary interest. Recently, marginal zero-inflated Poisson (MZIP; Long et al. [A marginalized zero-inflated Poisson regression model with overall exposure effects. Stat. Med. 33 (2014), pp. 5151–5165]) and negative binomial (MZINB; Preisser et al., 2016) models have been introduced that model the mean response directly. These models yield covariate effects that have simple interpretations that are, for many applications, more appealing than those available from ZI regression. This paper outlines a general framework for marginal zero-inflated models where the latent distribution is a member of the exponential dispersion family, focusing on common distributions for count data. In particular, our discussion includes the marginal zero-inflated binomial (MZIB) model, which has not been discussed previously. The details of maximum likelihood estimation via the EM algorithm are presented and the properties of the estimators as well as Wald and likelihood ratio-based inference are examined via simulation. Two examples presented illustrate the advantages of MZIP, MZINB, and MZIB models for practical data analysis.     

Multivariate Return Decomposition: Theory and Implications

In this paper, we propose a model based on multivariate decomposition of multiplicative – absolute values and signs – components of asset returns. In the m-variate case, the marginals for the m absolute values and the binary marginals for the m directions are linked through a 2m-dimensional copula. The approach is detailed in the case of a bivariate decomposition. We outline the construction of the likelihood function and the computation of different conditional measures. The finite-sample properties of the maximum likelihood estimator are assessed by simulation. An application to predicting bond returns illustrates the usefulness of the proposed method.     

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.     

Conditional covariance penalties for mixed models

The prediction error for mixed models can have a conditional or a marginal perspective depending on the research focus. We introduce a novel conditional version of the optimism theorem for mixed models linking the conditional prediction error to covariance penalties for mixed models. Different possibilities for estimating these conditional covariance penalties are introduced. These are bootstrap methods, cross-validation, and a direct approach called Steinian. The behavior of the different estimation techniques is assessed in a simulation study for the binomial-, the t-, and the gamma distribution and for different kinds of prediction error. Furthermore, the impact of the estimation techniques on the prediction error is discussed based on an application to undernutrition in Zambia.     

三种运营模式下O2O外卖服务供应链质量努力策略研究

O2O外卖作为"互联网+"时代下的新型餐饮模式，日益受到人们的关注。本文研究的O2O外卖服务供应链由一个O2O外卖平台、一个餐饮商家和一个配送骑手构成。O2O外卖平台决策平台的服务质量努力和服务费用，餐饮商家决策产品价格，骑手决策配送服务质量努力程度，同时会受到来自平台或商家的服务质量激励。通过分析商家配送、平台配送和商家自建平台+配送三种运营模式，分别构建O2O外卖服务供应链成员的利润函数，运用博弈理论，优化得到不同模式下O2O外卖服务供应链成员的最优质量控制策略和最优利润。利用公式推导和对比分析，分别探讨了骑手激励金额上限和市场规模对服务供应链最优策略和最优利润的影响。通过数值仿真，可得结论：服务供应链各成员利润均随着市场规模的增大而增大，随着骑手激励金额上限的增加而先增大后减少。当市场规模较小时，平台配送模式下商家最优利润最大。而当市场规模较大时，商家自建平台+配送模式下商家最优利润最大。     

The (non-)application of blockchain technology in the Greek shipping industry

The implementation of blockchain technology (BCT) is gaining traction in supply chain networks, revolutionising the operation of contemporary supply chains and reshaping the potential of business relationships. Empirical studies on blockchain adoption are scant because implementation across networks is in fairly early phase of development, yet evidence from empirical studies is highly desirable. This is one of the first studies of blockchain adoption in the Greek shipping industry, which has not so far been examined by the literature, in direct comparison to early adopters in other European countries such as Norway. The research examined eight Greek shipping companies using workshops with experienced supply chain personnel. Qualitative analysis identified the current position of these organisations in terms of blockchain adoption, by considering possible benefits and inhibitors to implementation. Despite benefits of automated processes and reduced paperwork as a result of smart contracts, findings show a reluctance to adopt BCT. That is, enterprise resource planning (ERP) transformations have left organisations fatigued and disinclined towards further systems development and resistant to subsequent change. Also, the exposure of shared information in the shipping nexus is considered to cause a threat to competitive survival.