“生态整体主义”对文学批评的意义

“生态整体主义”对文学批评的意义表现在文学批评标准和文学批评理论思维方式这两个方面；是对这两个方面可能存在的“中心论”与“二元论”的超越!只有不预设中心，才能平等对待不同的文学批评对象；只有将线性的、孤立的、有序的思维拓展到整体论的、复杂性的思维，文学批评才可能具有创新意味。     

母语在藏族大学生识记中的作用

通过对藏族大学生在识记不同语言材料时表现出不同效果的分析,从信息加工的角度阐述了母语是识记的基础,是识记表象建立的关键,母语所反映的认知取向决定了识记的侧重点。     

中国西部民族地区农村城镇化发展模式研究——以藏族地区九寨沟县为例

中国西部民族地区九寨沟县候鸟型的旅游推进型城镇化模式,与城镇化推进的经典理论,工业化是城镇化的根本动力,有截然不同的表现形式。城镇人口的增长,呈现明显的季节性,随旅游旺季淡季的变化呈现一种规律性的潮汐式的变化方式。前往旅游的流动人口数量是城镇常住人口的数倍甚至10倍以上。这种城镇化推进过程,所推进发展的,作为增长极的城镇,所产生的聚集扩散效应作用却是基本一致的。     

Signaling to Partially Informed Investors in the Newsvendor Model

We analyze a signaling game between the manager of a firm and an investor in the firm. The manager has private information about the firm's demand and cares about the short‐term stock price assigned by the investor. Previous research has shown that under continuous decision choices and the Intuitive Criterion refinement, the least‐cost separating equilibrium will result, in which a low‐quality firm chooses its optimal capacity and a high‐quality firm over‐invests in order to signal its quality to investors. We build on this research by showing the existence of pooling outcomes in which low‐quality firms over‐invest and high‐quality firms under‐invest so as to provide identical signals to investors. The pooling equilibrium is practically appealing because it yields a Pareto improvement compared to the least‐cost separating equilibrium. Distinguishing features of our analysis are that: (i) we allow the capacity decision to have either discrete or continuous support, and (ii) we allow beliefs to be refined based on either the Undefeated refinement or the Intuitive Criterion refinement. We find that the newsvendor model parameters impact the likelihood of a pooling outcome, and this impact changes in both sign and magnitude depending on which refinement is used.     

Bounding Optimal Expected Revenues for Assortment Optimization under Mixtures of Multinomial Logits

We consider assortment problems under a mixture of multinomial logit models. There is a fixed revenue associated with each product. There are multiple customer types. Customers of different types choose according to different multinomial logit models whose parameters depend on the type of the customer. The goal is to find a set of products to offer so as to maximize the expected revenue obtained over all customer types. This assortment problem under the multinomial logit model with multiple customer types is NP‐complete. Although there are heuristics to find good assortments, it is difficult to verify the optimality gap of the heuristics. In this study, motivated by the difficulty of finding optimal solutions and verifying the optimality gap of heuristics, we develop an approach to construct an upper bound on the optimal expected revenue. Our approach can quickly provide upper bounds and these upper bounds can be quite tight. In our computational experiments, over a large set of randomly generated problem instances, the upper bounds provided by our approach deviate from the optimal expected revenues by 0.15% on average and by less than one percent in the worst case. By using our upper bounds, we are able to verify the optimality gaps of a greedy heuristic accurately, even when optimal solutions are not available.     

Semiparametric regression methods for temporal processes subject to multiple sources of censoring

Process regression methodology is underdeveloped relative to the frequency with which pertinent data arise. In this article, the response-190 is a binary indicator process representing the joint event of being alive and remaining in a specific state. The process is indexed by time (e.g., time since diagnosis) and observed continuously. Data of this sort occur frequently in the study of chronic disease. A general area of application involves a recurrent event with non-negligible duration (e.g., hospitalization and associated length of hospital stay) and subject to a terminating event (e.g., death). We propose a semiparametric multiplicative model for the process version of the probability of being alive and in the (transient) state of interest. Under the proposed methods, the regression parameter is estimated through a procedure that does not require estimating the baseline probability. Unlike the majority of process regression methods, the proposed methods accommodate multiple sources of censoring. In particular, we derive a computationally convenient variant of inverse probability of censoring weighting based on the additive hazards model. We show that the regression parameter estimator is asymptotically normal, and that the baseline probability function estimator converges to a Gaussian process. Simulations demonstrate that our estimators have good finite sample performance. We apply our method to national end-stage liver disease data. The Canadian Journal of Statistics 48: 222–237; 2020 © 2019 Statistical Society of Canada     

Bayesian inference for zero-and-one-inflated geometric distribution regression model using Pólya-Gamma latent variables

Abstract

In the fields of internet financial transactions and reliability engineering, there could be more zero and one observations simultaneously. In this paper, considering that it is beyond the range where the conventional model can fit, zero-and-one-inflated geometric distribution regression model is proposed. Ingeniously introducing Pólya-Gamma latent variables in the Bayesian inference, posterior sampling with high-dimensional parameters is converted to latent variables sampling and posterior sampling with lower-dimensional parameters, respectively. Circumventing the need for Metropolis-Hastings sampling, the sample with higher sampling efficiency is obtained. A simulation study is conducted to assess the performance of the proposed estimation for various sample sizes. Finally, a doctoral dissertation data set is analyzed to illustrate the practicability of the proposed method, research shows that zero-and-one-inflated geometric distribution regression model using Pólya-Gamma latent variables can achieve better fitting results.     

Parameter estimation of Lindley step stress model with independent competing risk under type 1 censoring

Abstract

In literature, Lindley distribution is considered as an alternative to exponential distribution to fit lifetime data. In the present work, a Lindley step-stress model with independent causes of failure is proposed. An algorithm to generate random samples from the proposed model under type 1 censoring scheme is developed. Point and interval estimation of the model parameters is carried out using maximum likelihood method and percentile bootstrap approach. To understand the effectiveness of the resulting estimates, numerical illustration is provided based on simulated and real-life data sets.     

供需不确定条件下基于CVaR的零售商库存鲁棒优化模型

在经典报童模型下考虑供应和需求不确定性,研究了具有风险厌恶的零售商库存优化问题。采用条件风险值（CVaR）对库存绩效进行度量,构建了基于CVaR的零售商库存运作模型;在此基础上,考虑上游供应商供货能力和下游市场需求不确定性,并采用一系列未知概率的离散情景进行描述,给出了供需不确定条件下基于CVaR的零售商库存鲁棒优化模型。进一步,采用区间不确定集对未知情景概率进行建模,给出了基于最大最小准则的鲁棒对应模型。针对同时考虑供需不确定性导致的模型非凸性,采用标准对偶理论将其转化为易于求解的数学规划问题。最后,通过数值计算分析了不同风险厌恶程度和不确定性程度对零售商库存决策以及库存绩效的影响。结果表明,供需不确定性的存在虽然会导致零售商库存绩效损失,但损失值较小。特别地,依据文中模型得到的鲁棒库存策略在多数情况下能够保证零售商获得更优的库存绩效。此外,不确定性和风险厌恶程度的增加虽然会影响零售商库存决策和运作绩效,但在同等风险厌恶态度下,随着不确定性程度的增加,基于文中方法得到的鲁棒库存策略仍能确保零售商获得理想的库存绩效,表明文中所建模型在应对供需不确定性方面具有良好的鲁棒性。     

基于灰色系统建模技术的人体疾病早期预测预警研究

传统健康体检主要通过对单次体检指标进行横向比较和静态分析,忽略个体差异,纯粹从指标高低来判断体检者健康状况与身体状态,难以及时发现体检者可能存在的疾病隐患。由于个体体检指标具有样本量小、信息不确定、数据类型异构、影响因素构成复杂等特征,传统以大样本为基础的数学模型均难以适应此类小数据系统的建模要求。为此,通过建立适用于人体主要指标趋势预测的灰色系统模型（简称HIGM（1,1）模型）,实现对人体主要健康指标的动态建模与趋势分析,从而可以掌握体检者未来一段时间身体指标的变化趋势及可能存在的疾病隐患。研究成果对提升体检效果、强化体检意义,促进灰色理论与现实问题的有效对接等方面,均具有积极意义。