宋金时期安多藏族人口的数据与统计——兼谈宋金时期安多藏族人口发展的原因

宋金时期,今青海、甘肃及四川西北部的安多地区,是藏族居住的一个重要区域,分布着大量藏族人口。本文拟对宋金文献中出现的安多藏族人口数据进行深入细致的分析,力求对当时安多藏族人口数据及人口发展的原因得出与史实相符的认识。     

陆荣廷政权在边疆民族经济融合进程中运用的政府和民间两种力量

陆荣廷为首的桂系集团是一个复杂的政治群体,对其历史作用应进行多元化评价。他们利用政府和民间两种力量清除了部分落后的社会政治制度和邪恶势力,扩大了近代经济制度和社会经济组织的影响,在一定程度上奠定了广西新的政治和经济格局。同时推动了边疆民族经济融合的程度进一步加深。     

No‐Bubble Condition: Model‐Free Tests in Housing Markets

We test for the existence of housing bubbles associated with a failure of the transversality condition that requires the present value of payments occurring infinitely far in the future to be zero. The most prominent such bubble is the classic rational bubble. We study housing markets in the United Kingdom and Singapore, where residential property ownership takes the form of either leaseholds or freeholds. Leaseholds are finite‐maturity, pre‐paid, and tradeable ownership contracts with maturities often exceeding 700 years. Freeholds are infinite‐maturity ownership contracts. The price difference between leaseholds with extremely‐long maturities and freeholds reflects the present value of a claim to the freehold after leasehold expiry, and is thus a direct empirical measure of the transversality condition. We estimate this price difference, and find no evidence of failures of the transversality condition in housing markets in the U.K. and Singapore, even during periods when a sizable bubble was regularly thought to be present.     

面板数据的贝叶斯Elastic Net分位数回归方法及其应用研究

本文首次将Elastic Net这种用于高度相关变量的惩罚方法用于面板数据的贝叶斯分位数回归,并基于非对称Laplace先验分布推导所有参数的后验分布,进而构建Gibbs抽样。为了验证模型的有效性,本文将面板数据的贝叶斯Elastic Net分位数回归方法(BQR. EN)与面板数据的贝叶斯分位数回归方法(BQR)、面板数据的贝叶斯Lasso分位数回归方法(BLQR)、面板数据的贝叶斯自适应Lasso分位数回归方法(BALQR)进行了多种情形下的全方位比较,结果表明BQR. EN方法适用于具有高度相关性、数据维度很高和尖峰厚尾分布特征的数据。进一步地,本文就BQR. EN方法在不同扰动项假设、不同样本量的情形展开模拟比较,验证了新方法的稳健性和小样本特性。最后,本文选取互联网金融类上市公司经济增加值(EVA)作为实证研究对象,检验新方法在实际问题中的参数估计与变量选择能力,实证结果符合预期。     

食管癌患者健康相关生命质量研究

目的:探讨食管癌患者的健康相关生命质量现状及影响因素?方法:采用一般资料问卷?欧洲五维度健康评定量表(EQ-5D量表),对209例食管癌患者进行问卷调查,采用单因素分析和多因素分析探讨食管癌患者健康相关生命质量的影响因素? 结果:食管癌患者行动?自我照顾?日常活动?疼痛/不适?焦虑/抑郁五个维度存在问题的比例分别为18.2%?12.0%?22.0%?38.3%?25.4%;自评健康得分(VAS评分)为(75.20 ± 11.00)分;综合健康指数得分(Index得分)为(0.84 ± 0.22)分?患者的性别?家庭年收入?文化程度是VAS评分的主要影响因素;患者的年龄?治疗方式?肿瘤分期?医保类型是Index得分的主要影响因素?结论:实施心理干预?早诊早治?大病医疗保险有助于提高食管癌患者健康相关生命质量?     

A visualization method measuring the performance of biomarkers for guiding treatment decisions

Biomarkers that predict efficacy and safety for a given drug therapy become increasingly important for treatment strategy and drug evaluation in personalized medicine. Methodology for appropriately identifying and validating such biomarkers is critically needed, although it is very challenging to develop, especially in trials of terminal diseases with survival endpoints. The marker‐by‐treatment predictiveness curve serves this need by visualizing the treatment effect on survival as a function of biomarker for each treatment. In this article, we propose the weighted predictiveness curve (WPC). Based on the nature of the data, it generates predictiveness curves by utilizing either parametric or nonparametric approaches. Especially for nonparametric predictiveness curves, by incorporating local assessment techniques, it requires minimum model assumptions and provides great flexibility to visualize the marker‐by‐treatment relationship. WPC can be used to compare biomarkers and identify the one with the highest potential impact. Equally important, by simultaneously viewing several treatment‐specific predictiveness curves across the biomarker range, WPC can also guide the biomarker‐based treatment regimens. Simulations representing various scenarios are employed to evaluate the performance of WPC. Application on a well‐known liver cirrhosis trial sheds new light on the data and leads to discovery of novel patterns of treatment biomarker interactions. Copyright © 2015 John Wiley & Sons, Ltd.     

油气田边远地区天然气资源的综合利用研究

油气田边远地区天然气资源包括油田放空天然气(主要是油田伴生气)和低产气井天然气,它们均因气量小,就地无用户和远离输气管网等原因长期未得到开发利用,不仅极大地浪费了资源,而且严重地污染了油田地区的大气。综述了国内外可用于油气田边远地区天然气资源开发利用的净化技术和综合利用技术,认为利用这部分天然气资源应依据气质、气量和市场需求选用合理的净化技术及利用技术,最后对吐哈油田边远地区天然气资源的综合利用进行了研究。     

Lassoing the Determinants of Retirement

This article uses Danish register data to explain the retirement decision of workers in 1990 and 1998. Many variables might be conjectured to influence this decision such as demographic, socioeconomic, financial, and health related variables as well as all the same factors for the spouse in case the individual is married. In total, we have access to 399 individual specific variables that all could potentially impact the retirement decision. We use variants of the least absolute shrinkage and selection operator (Lasso) and the adaptive Lasso applied to logistic regression in order to uncover determinants of the retirement decision. To the best of our knowledge, this is the first application of these estimators in microeconometrics to a problem of this type and scale. Furthermore, we investigate whether the factors influencing the retirement decision are stable over time, gender, and marital status. It is found that this is the case for core variables such as age, income, wealth, and general health. We also point out the most important differences between these groups and explain why these might be present.     

Power analysis for clustered non-continuous responses in multicenter trials

Power analysis for multi-center randomized control trials is quite difficult to perform for non-continuous responses when site differences are modeled by random effects using the generalized linear mixed-effects model (GLMM). First, it is not possible to construct power functions analytically, because of the extreme complexity of the sampling distribution of parameter estimates. Second, Monte Carlo (MC) simulation, a popular option for estimating power for complex models, does not work within the current context because of a lack of methods and software packages that would provide reliable estimates for fitting such GLMMs. For example, even statistical packages from software giants like SAS do not provide reliable estimates at the time of writing. Another major limitation of MC simulation is the lengthy running time, especially for complex models such as GLMM, especially when estimating power for multiple scenarios of interest. We present a new approach to address such limitations. The proposed approach defines a marginal model to approximate the GLMM and estimates power without relying on MC simulation. The approach is illustrated with both real and simulated data, with the simulation study demonstrating good performance of the method.