宋、金时期安多藏族部落包家族考述

宋、金时期,安多藏族中有一支著名的吐蕃汉姓部落“包家族”,其来源是吐蕃化的汉姓丁家,最初居地在秦州宁远砦北,后迁徙于熙、河、洮、岷等地,并从此世居此地,其家族成员有宋代的包约、包顺、包诚及金朝的包长寿、包世显等。包家族归附宋、金王朝后,始终效力于宋、金王朝,与宋、金王朝保持着密切的关系。     

从《野鬼的年代》看西南中国

20世纪80年代以来,不少海外人类学家对中国社会研究的兴趣逐渐转移到了西南少数民族地区,一些学者甚至从汉人社会的研究转到西南少数民族的研究与探讨上来.近二十年来,在国外人类学家对中国西南的研究中,不仅涌现了大量的作品,且关注的问题也十分广泛,从民族认同到历史记忆、文化展示等问题都有涉及.     

试论城市化进程中的民族关系--以对临夏市的调查为视点

临夏市作为西北民族地区的一个小城市,在城市化进程中,其民族关系也会受到影响.针对临夏市的实际情况及学术界的研究现状,我们选取临夏市城市化进程中变化较大的几个变量,即民族居住格局的变迁、人口流动的影响、民族通婚的增减以及宗教因素,作为研究重点来衡量城市化进程中的民族关系.在此基础上得出临夏市各民族之间的关系总体上是平等、团结、互助、合作的社会主义新型民族关系,和谐平等的民族关系占据了主导地位的结论,并归纳出城市化进程中临夏市的民族关系具有主从性、敏感性、易发性、历史性、隐蔽性、复杂性等特点,最后提出临夏市各族群众必须解决自身的定位问题,建立临夏市和谐民族关系的良性运转机制.     

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。     

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。     

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.     

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.