逆向选择条件下带奖惩金的两期保险契约模型及其Pareto改进性研究

为了更有效的规避影响保险市场交易效率的逆向选择问题，本文分投保人风险类型为两种和多种情形建立了带奖惩金的两期保险契约模型，首次提出可以用奖励金和惩罚金有效甄别投保人的风险类型。该模型根据投保人第一个保险期内的索赔情况在第二个保险期对其进行奖励或惩罚，高风险类型的投保人如果选择为低风险类型投保人设计的保险契约，则其在第二阶段受到惩罚的概率要远远大于得到奖励的概率，即风险越高的投保人越害怕惩罚金，因此所建模型满足斯彭斯-莫里斯分离条件。带奖惩金的两期保险契约模型中保险公司的期望利润仍然为0，并不会给投保人带来额外的经济负担，却能够实现对传统部分保险契约简单重复两次的严格帕累托改进。最后采用一个算例说明了该模型的有效性。     

Production systems and supplier selection: a multi-phase process model

Abstract

Purpose: This paper presents a combined multi-phase supplier selection model. The process repeatedly revisits the criteria and sourcing decision as the development process continues. This enables a structured adoption of product and production system innovation from strategic suppliers, where previously the literature purely focuses on product innovation or cost reduction. Design/methodology/approach: The authors adopted an embedded researcher style, inductive, qualitative case study of an industrial supply cluster comprising a focal automotive company and its interaction with three different strategic stamping suppliers. Findings: Our contribution is the multi-phased production and product innovation process. This is an advance from traditional supplier selection and also an extension of ideas of supplier-located product development as it includes production system development, and complements the literature on working with strategic suppliers. Specifically, we explicitly articulate the previously unreported issue of whether a supplier chosen for its innovation capabilities at the start of the new product development process will also be the most appropriate supplier during the production system development phase, when an ability to work collaboratively may be the most important attribute, or in the large-scale production phase when an ability to manufacture at low unit cost may be most important. Originality/value: The paper identifies a multi-phase approach to tendering within a fixed body of strategic suppliers which seeks to identify the optimum technological and process decisions as well as the traditional supplier sourcing choice. These areas have not been combined before and generate a valuable approach for firms to adopt as well as for researchers to extend our understanding of a highly complex process.     

The dual-continuum approach: An extension of the contingency theory of strategic conflict management

This article examines the limitations of using a single advocacy-accommodation continuum when depicting organizational stance and movement in conflict situations (Cancel et al., 1997). The authors argue that advocacy and accommodation can vary independently in response to a variety of contingent factors. To comprehensively capture the locations and motions that are possible in organization-public conflicts, a two-continuum approach is proposed. Separate assessment of the effects of contingency variables on advocacy and accommodation can be used to accurately locate organizational stance along advocacy and accommodation continua. Strategic alternatives that cannot be depicted using a single continuum (such as favorable alignment of organization advocacy/public accommodation or unilateral change on the part of one party) can be captured if separate continua are employed. By depicting initial stances and desired directions of movement for both the organization and public, the dual-continuum approach can also provide practical guidance to public relations professionals in selecting strategies for achieving preferred outcomes and evaluating their effectiveness.     

Conditional Akaike Information Criteria for a Class of Poisson Mixture Models with Random Effects

Focusing on the model selection problems in the family of Poisson mixture models (including the Poisson mixture regression model with random effects and zero‐inflated Poisson regression model with random effects), the current paper derives two conditional Akaike information criteria. The criteria are the unbiased estimators of the conditional Akaike information based on the conditional log‐likelihood and the conditional Akaike information based on the joint log‐likelihood, respectively. The derivation is free from the specific parametric assumptions about the conditional mean of the true data‐generating model and applies to different types of estimation methods. Additionally, the derivation is not based on the asymptotic argument. Simulations show that the proposed criteria have promising estimation accuracy. In addition, it is found that the criterion based on the conditional log‐likelihood demonstrates good model selection performance under different scenarios. Two sets of real data are used to illustrate the proposed method.     

Sparse group lasso for multiclass functional logistic regression models

Sparsity-inducing penalties are useful tools for variable selection and are also effective for regression problems where the data are functions. We consider the problem of selecting not only variables but also decision boundaries in multiclass logistic regression models for functional data, using sparse regularization. The parameters of the functional logistic regression model are estimated in the framework of the penalized likelihood method with the sparse group lasso-type penalty, and then tuning parameters for the model are selected using the model selection criterion. The effectiveness of the proposed method is investigated through simulation studies and the analysis of a gene expression data set.     

Revanchist Masculinity: Gender Attitudes in Sex Work Management

Pimps, or male managers of female sex workers, are commonly represented in popular culture as hypermasculine and as a ubiquitous part of sex work. However, there is little empirical scholarship on pimps or the construction of their masculinity. Drawing on ethnographic and interview data, this article demonstrates how pimps produce a “revanchist masculinity” that seeks to reclaim power from women and establish status over other men. Pimps are suspicious of sex workers’ motives and deny them decision‐making power and profit sharing—processes that highlight how work practices can structure gender identity construction.     

An m-estimation-based model selection criterion with a data-oriented penalty

In Wu and Zen (1999), a linear model selection procedure based on M-estimation is proposed, which includes many classical model selection criteria as its special cases, and it is shown that the selection procedure is strongly consistent for a variety of penalty functions. In this paper, we will investigate its small sample performances for some choices of fixed penalty functions. It can be seen that the performance varies with the choice of the penalty. Hence, a randomized penalty based on observed data is proposed, which preserves the consistency property and provides improved performance over a fixed choice of penalty functions.     

Visibility That Demystifies: Gays,Gender, and Sex on Television

A content analysis of 98 episodes of primetime entertainment programs on commercial broadcast and cable television stations from the 2005–2006 season showed that gay characters on television were more likely to be shown in sexual situations than straight characters, and women were more likely to be shown in same-sex sexual situations than men. In addition, gay characters were more likely to be depicted as sexually active on cable television than they were on commercial broadcast television, and they were more likely to be relegated to guest star status on commercial broadcast television than on cable television. The study also showed that gay characters made up 7.5% of all the characters studied. This study discusses the implications of these findings for gay and straight audiences.     

论我国量刑程序模式的选择

量刑程序与定罪程序一样是刑事审判环节,具有独立性与依附性特征,而我国立法层面的量刑程序几乎是空白。我国在现有的司法环境下实行相对独立的量刑程序符合国际司法潮流,这一程序也没有增加各诉讼参加人的负担,较易操作落实,且有相关的配套程序和实践基础,因此具有可行性。但在实践中应注意一些问题,如要清晰控辩审三方在量刑程序中的定位,使社会调查报告机制走向制度化、专业化,判决书中应对量刑的依据和理由进行阐述等。     

Testing for linearity in generalized linear models using kernel smoothing

A test statistic proposed by Li (1999) for testing the adequacy of heteroscedastic nonlinear regression models using nonparametric kernel smoothers is applied to testing for linearity in generalized linear models. Simulation results for models with centered gamma and inverse Gaussian errors are presented to illustrate the performance of the resulting test compared with log-likelihood ratio tests for specific parametric alternatives. The test is applied to a data set of coronary heart disease status (Hosmer and Lemeshow, (1990).