The predictive Lasso

We propose a shrinkage procedure for simultaneous variable selection and estimation in generalized linear models (GLMs) with an explicit predictive motivation. The procedure estimates the coefficients by minimizing the Kullback-Leibler divergence of a set of predictive distributions to the corresponding predictive distributions for the full model, subject to an l ₁ constraint on the coefficient vector. This results in selection of a parsimonious model with similar predictive performance to the full model. Thanks to its similar form to the original Lasso problem for GLMs, our procedure can benefit from available l ₁-regularization path algorithms. Simulation studies and real data examples confirm the efficiency of our method in terms of predictive performance on future observations.     

Variational approximation for heteroscedastic linear models and matching pursuit algorithms

Modern statistical applications involving large data sets have focused attention on statistical methodologies which are both efficient computationally and able to deal with the screening of large numbers of different candidate models. Here we consider computationally efficient variational Bayes approaches to inference in high-dimensional heteroscedastic linear regression, where both the mean and variance are described in terms of linear functions of the predictors and where the number of predictors can be larger than the sample size. We derive a closed form variational lower bound on the log marginal likelihood useful for model selection, and propose a novel fast greedy search algorithm on the model space which makes use of one-step optimization updates to the variational lower bound in the current model for screening large numbers of candidate predictor variables for inclusion/exclusion in a computationally thrifty way. We show that the model search strategy we suggest is related to widely used orthogonal matching pursuit algorithms for model search but yields a framework for potentially extending these algorithms to more complex models. The methodology is applied in simulations and in two real examples involving prediction for food constituents using NIR technology and prediction of disease progression in diabetes.     

科技术语在线检索策略的实证探究

术语在线检索能力对科技翻译实践和人才培养均具有现实意义。通过对英语专业学生翻译作业的调查,厘清了翻译学习者在术语检索中存在的主要困难:缺乏术语识别力、缺乏对同义术语及多义术语的鉴别力,并从翻译技能和课程角度提出应对术语翻译的总体策略。     

组织学习动态过程障碍的整合研究

在对相关研究进行系统回顾的基础上,将组织学习过程障碍的分析层次从组织内的员工个体、团队、组织层次拓展到了组织外的社会环境层次,提出了组织学习动态过程障碍系统分析模型。研究结果理清了直觉感知、解释说明、归纳整合和制度化等组织学习子过程的障碍要素,以及各种障碍要素彼此间的关联及其对组织学习能力的影响和作用机理。由此,提出了相关的研究命题,为未来的研究提供了指引。     

运用体验营销创造顾客需求

体验营销以“创造市场”理念为核心 ,运用客户体验模式和客户一起共同锁定市场。该理论系统的运用恰好能让企业超越找不到满意市场份额的困境。文章就体验营销的运用提出几点建议。     

“中华老字号”品牌发展滞后原因及其对策研究

“中华老字号”的发展问题备受各方人士的关注。本文分析了“中华老字号”品牌发展滞后的原因,即缺乏品牌 运作、品牌文化创新、品牌保护和有效的品牌传播,探讨了“中华老字号”品牌发展的对策。     

Stopping-time resampling for sequential Monte Carlo methods

Summary.  Motivated by the statistical inference problem in population genetics, we present a new sequential importance sampling with resampling strategy. The idea of resampling is key to the recent surge of popularity of sequential Monte Carlo methods in the statistics and engin-eering communities, but existing resampling techniques do not work well for coalescent-based inference problems in population genetics. We develop a new method called 'stopping-time resampling', which allows us to compare partially simulated samples at different stages to terminate unpromising partial samples and to multiply promising samples early on. To illustrate the idea, we first apply the new method to approximate the solution of a Dirichlet problem and the likelihood function of a non-Markovian process. Then we focus on its application in population genetics. All our examples show that the new resampling method can significantly improve the computational efficiency of existing sequential importance sampling methods.     

晚稻旱育秧综合配套技术研究

通过五元二次回归旋转组合设计试验，明确了影响洞庭湖区晚稻生产的主要因素，依次为播种量（X1），2．1叶期喷施效唑（X4）移栽叶龄（X2），分蘖期施纯氮（X5）等，建立了五元二次目标函数模型，提出了适应洞庭湖区一季晚稻和双季晚稻的五套优化综合配套技术，探讨了二因素对产量影响显著的交互效应。     

社会主义市场经济与中国当代社会思潮

本文主要就如何发挥市场经济的正面效应、抑制市场经济的负面效应，加强物质文明建设与精神文明的建设，引导青年学生树立正确的价值观和人生理想，实现具有中国特色的现代化目标谈几点看法。     

Implications for the Role of Retailers in Quality Assurance

We investigate a manufacturer–retailer channel to explore the role of a retailer in assuring the quality of a manufacturer's product as a quality gatekeeper. Such a gatekeeping activity can entail a reduction in the defective rate for consumers, if the retailer charges the manufacturer a penalty for each identified defect that is no smaller than the market penalty for an unidentified defect. As a result of the retailer's gatekeeping, the change in the negotiated wholesale price only depends on the manufacturer's individual benefit, whereas the change in the retailer's optimal retail price is associated with the channel‐wide benefit. When the impact of quality relative to retail price on demand is higher, the retailer benefits more from her gatekeeping activity, thus having a greater incentive to take on the quality gatekeeping responsibility. Moreover, the retailer's gatekeeping generates a larger increase in the demand as well as each firm's profit, when the retailer has a stronger relative bargaining power.